Subscribe: Geoscience and Remote Sensing Letters, IEEE - new TOC
http://ieeexplore.ieee.org/rss/TOC8859.XML
Added By: Feedage Forager Feedage Grade C rated
Language: English
Tags:
band  based  classification  data  high  hyperspectral  images  letter  method  model  proposed method  proposed  radar  results 
Rate this Feed
Rate this feedRate this feedRate this feedRate this feedRate this feed
Rate this feed 1 starRate this feed 2 starRate this feed 3 starRate this feed 4 starRate this feed 5 star

Comments (0)

Feed Details and Statistics Feed Statistics
Preview: Geoscience and Remote Sensing Letters, IEEE - new TOC

IEEE Geoscience and Remote Sensing Letters - new TOC



TOC Alert for Publication# 8859



 



Front Cover

Nov. 2017

Presents the front cover for this issue of the publication.



IEEE Geoscience and Remote Sensing Letters publication information

Nov. 2017

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.



Table of contents

Nov. 2017

Presents the table of contents for this issue of the publication.



The Iterative Reweighted Alternating Direction Method of Multipliers for Separating Structural Layovers in SAR Tomography

Nov. 2017

Layover scatterers of tall building structures can be separated by synthetic aperture radar tomography (SAR-tomo). An iterative reweighted L1 minimization (IRL1) has been applied to enhance the sparsity in a tomographic inversion, where the basis pursuit (BP) technique was adopted to search for the solution. However, the IRL1 with BP is highly time-consuming, which may prevent its real application to large-scale data sets. In this letter, we propose the iterative reweighted alternating direction method of multipliers (IR-ADMM) for fast SAR-tomo imaging. We demonstrate and validate the enhanced sparsity and fast convergence of our IR-ADMM algorithm with experiments using both simulated data and TerraSAR-X Stripmap images of tall urban buildings. The experimental results show that compared with conventional IR-BP, the IR-ADMM greatly reduces the computation time without substantial performance degradation.



Improvement of Handheld Radar Operators’ Hazard Detection Performance Using 3-D Visualization

Nov. 2017

Handheld ground-penetrating radar systems are employed in both military and humanitarian demining operations. Radar system operators are given the difficult job of determining the nature of subsurface objects from signal reflections in real time. Current systems require operators to multitask both collection and classification. This letter tested a 3-D data visualization method against a 2-D method. The 3-D method attempts to separate tasks by not forcing operators to classify objects in real time. Data showed that users classifying objects with the 3-D system had better performance and also reported that this system was more user-friendly. In addition, users were able to classify underground objects quicker with the 3-D system. The results of this letter demonstrate the benefit of 3-D visualization in ground scanning systems in increasing performance and decreasing cognitive load.



An Accurate Two-Step ISAR Cross-Range Scaling Method for Earth-Orbit Target

Nov. 2017

Inverse synthetic aperture radar (ISAR) cross-range scaling is used to obtain the actual cross-range size of the target, which is essential for space surveillance and automatic target recognition. In this letter, a novel two-step ISAR cross-range scaling method for earth-orbit targets is proposed, which improves the computational efficiency through the use of prior information and achieves high estimation accuracy. An initial rotation velocity (RV) is calculated first using the open two-line element data of the satellite orbit to coarsely achieve a cross-range scaling of the ISAR image with high efficiency. Then, the refined cross-range scaling result is obtained with an accurate RV, which is accomplished by the isolated scatterer extraction and the chirp-rate estimation, wherein the blob detection and the integrated cubic phase function are employed, respectively. The initial RV is used to narrow the search width of the chirp-rate estimation, and the corresponding computational burden is expected to decrease accordingly. Finally, simulations and real-data experiments are performed to verify the effectiveness and the accuracy of the proposed method.



A Nonparametric Statistical Technique for Modeling Overland TMI (2A12) Rainfall Retrieval Error

Nov. 2017

In this letter, we evaluate a nonparametric error model for Tropical Rainfall Measurement Mission (TRMM) passive microwave (PMW) rainfall (2A12) product over coverage in the southern continental United States, and assess the impact of surface soil moisture information on the model's performance. Reference precipitation was based on high-resolution (5 min/1 km) rainfall fields derived from the NOAA/National Severe Storms Laboratory multiradar multisensor system. The error model was evaluated using a K-fold validation experiment using systematic and random error statistics of the model-adjusted TRMM Microwave Imager rainfall point estimates, and ensemble verification statistics of the corresponding prediction intervals. Results show better performance, particularly in the accuracy of the prediction intervals, when near-surface soil moisture was used as input parameter. The error model can be extended using the TRMM and Global Precipitation Measurement satellite missions' precipitation radar rainfall and satellite soil moisture data sets to characterize globally the uncertainty of PMW products.



An Attitude Jitter Correction Method for Multispectral Parallax Imagery Based on Compressive Sensing

Nov. 2017

Attitude jitter is a common problem for high-resolution earth-observation satellites and can diminish the geo-positioning and mapping performance of observed images. It is especially necessary to address this problem when high-performance attitude measurements are unavailable. Therefore, an attitude jitter correction method for multispectral parallax imagery that utilizes the compressive-sensing technology is proposed in this letter. In the proposed method, the attitude jitter is estimated from the parallax disparities of different band images, and then the image displacement caused by attitude jitter can be corrected. Using the normalized cross correlation method and compressive-sensing technology, the proposed method can deal with the condition of texture-feature deficiency in the partial image. The multispectral images of the Terra and ZY-3 satellites are used as experimental data to evaluate the proposed method. The registration errors of different bands are greatly reduced in both the cross- and along-track directions, and the experiment results indicate that the proposed method is effective for correcting the attitude jitter of both satellites.



HIRAD Brightness Temperature Image Geolocation Validation

Nov. 2017

The Hurricane Imaging Radiometer (HIRAD) is an airborne microwave radiometer developed to provide wide-swath hurricane surface wind speed and rain rate imagery for scientific research. This letter presents a geometric evaluation of the brightness temperature (Tb) images produced by HIRAD for high-contrast land/water targets. Methodologies used to validate geolocation accuracy and spatial resolution are discussed, and results are presented to provide quantitative pixel geolocation accuracy and the effective image spatial resolution of the Tb image.



Cross-Polarization Amplitudes of Obliquely Orientated Buildings With Application to Urban Areas

Nov. 2017

Buildings that are rotated with respect to the sensor trajectory could be erroneously classified as vegetated areas in the Pauli basis, and subsequently in many decomposition theorems despite the considerable amount of work done to solve that issue. This misjudgement is linked to the high level of their cross-polarized contribution. Using electromagnetic simulation tools and image analysis, we study the value of these cross-polarization components. We show that forested areas and cities exhibit significantly different cross-polarization levels; indeed, the origin of these components is actually distinct. Based on that, to discriminate between the two environments, we introduce an extension to the Pauli basis where the cross polarization is split into two classes, one for rotated dihedrals and the other for random scatterers. This approach is then tested on two synthetic aperture radar images: the first acquired at C-band using RADARSAT-2 over Downtown San Francisco and the second using RAMSES at X-band over an industrial area near Paris.



Comparative Analysis of Temporal Decorrelation at P-Band and Low L-Band Frequencies Using a Tower-Based Scatterometer Over a Tropical Forest

Nov. 2017

Temporal decorrelation is a critical parameter for repeat-pass coherent radar processing, including many advanced techniques such as polarimetric SAR interferometry (PolInSAR) and SAR tomography (TomoSAR). Given the multifactorial and unpredictable causes of temporal decorrelation, statistical analysis of long time series of measurements from tower-based scatterometers is the most appropriate method for characterizing how rapidly a specific scene decorrelates. Based on the TropiScat experiment that occurred in a tropical dense forest in French Guiana, this letter proposes a comparative analysis between temporal decorrelation at P-band and at higher frequencies in the range of 800-1000 MHz (the low end of the L-band). This letter aims to support the design of future repeat-pass spaceborne missions and to offer a better understanding of the physics behind temporal decorrelation. Beyond the expected lower values that are found and quantified at the low L-band compared with the P-band, similar decorrelation patterns related to rainy and dry periods are emphasized in addition to the critical impacts of acquisition time during the day.



Spatially Adaptive Sparse Representation for Target Detection in Hyperspectral Images

Nov. 2017

As sparse representation gradually obtains better and better results in the analysis of hyperspectral imagery and sparsity-based algorithms are becoming more and more popular, especially in target detection. However, these methods mostly assume an absolute equal contribution by all neighboring pixels while detecting the central pixel. There is no doubt that this approach is unsuitable for pixels located in heterogeneous areas. In this letter, to address this problem, spatially adaptive sparse representation for target detection in hyperspectral images (HSIs) is proposed. Neighboring spatial information is utilized by considering the different contributions of the distinct neighborhood pixels. The different weights are determined according to the similarity between the neighboring pixels and the central test pixel. The proposed algorithm was tested on two HSIs and demonstrated outstanding detection performance when compared with other commonly used detectors.



Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification

Nov. 2017

For hyperspectral image (HSI) classification, it is very important to learn effective features for the discrimination purpose. Meanwhile, the ability to combine spectral and spatial information together in a deep level is also important for feature learning. In this letter, we propose an unsupervised feature learning method for HSI classification, which is based on recursive autoencoders (RAE) network. RAE utilizes the spatial and spectral information and produces high-level features from the original data. It learns features from the neighborhood of the investigated pixel to represent the whole local homogeneous area of the image. In addition, to obtain more accurate representation of the investigated pixel, a weighting scheme is adopted based on the neighboring pixels, where the weights are determined by the spectral similarity between the neighboring pixels and the investigated pixel. The effectiveness of our method is evaluated by the experiments on two hyperspectral data sets, and the results show that our proposed method has a better performance.



Independent Encoding Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection

Nov. 2017

Target detection is playing an important role in hyperspectral image (HSI) processing. Many traditional detection methods utilize the discriminative information within all the single-band images to distinguish the target and the background. The critical challenge with these methods is simultaneously reducing spectral redundancy and preserving the discriminative information. The multitask learning (MTL) technique has the potential to solve the aforementioned challenge, since it can further explore the inherent spectral similarity between the adjacent single-band images. This letter proposes an independent encoding joint sparse representation and an MTL method. This approach has the following capabilities: 1) explores the inherent spectral similarity to construct multiple sub-HSIs in order to reduce spectral redundancy for each sub-HSI; 2) takes full advantage of the prior class label information to construct reasonable joint sparse representation and MTL models for the target and the background; 3) explores the great difference between the target dictionary and background dictionary with different regularization strategies in order to better encode the task relatedness for two joint sparse representation and MTL models; and 4) makes the detection decision by comparing the reconstruction residuals under different prior class labels. Experiments on two HSIs illustrated the effectiveness of the proposed method.



Weighted Low-Rank Representation-Based Dimension Reduction for Hyperspectral Image Classification

Nov. 2017

A predimension-reduction algorithm that couples weighted low-rank representation (WLRR) with a skinny intrinsic mode functions (IMFs) dictionary is proposed for hyperspectral image (HSI) classification. It seeks a low-rank subspace to solve the performance degradation issue encountered by linear discriminant analysis in a small-sample-size situation. It can also improve the scatter matrix estimation when using a large training set. Unlike those commonly used methods, e.g., the principal component analysis-based ones, WLRR focuses on preserving more structure information. Based on the traditional LRR model, WLRR introduces a local weighted regularization to characterize the correlation between samples such that HSI-specific local structure can be better preserved as well as its global structure. Indeed, more structure information gives more additional discriminant ability. Furthermore, a new discriminant IMFs dictionary is designed to enhance interclass difference via empirical mode decomposition. The proposed method is investigated on several HSI data sets. All experimental results prove it a competitive and promising predimension-reduction means when compared to other traditional techniques.



Optimal Illumination and Color Consistency for Optical Remote-Sensing Image Mosaicking

Nov. 2017

Illumination and color consistency are very important for optical remote-sensing image mosaicking. In this letter, we propose a simple but effective technique that simultaneously performs image illumination and color correction for multiview images. In this framework, we first present an uneven illumination removal algorithm based on bright channel prior, which guarantees the illumination consistency inside a single image. We then adapt a pairwise color-correction method to coarsely align the color tone between source and reference images. In this stage, we give a new single-image quality metric which combines brightness deviation, color cast, and entropy together for automatic reference-image selection. Finally, we perform a least-squares adjustment (LSA) procedure to obtain optimal illumination and color consistency among multiview images. In detail, we first perform a pairwise image matching by using SIFT algorithm; once sparse local patch correspondences obtained, the illumination and color relationship between images can be established based on a global gamma correction model; the illumination and color errors can then be minimized by LSA. Extensive experiments on both challenging synthetic and real optical remote-sensing image data sets show that it significantly outperforms the compared state-of-the-art approaches. All the source code and data sets used in this letter are made public.



Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier–Feature Assembly

Nov. 2017

Sea ice type is one of the most sensitive variables in Arctic ice monitoring and detailed information about it is essential for ice situation evaluation, vessel navigation, and climate prediction. Many machine-learning methods including deep learning can be employed for ice-type detection, and most classifiers tend to prefer different feature combinations. In order to find the optimal classifier-feature assembly (OCF) for sea ice classification, it is necessary to assess their performance differences. The objective of this letter is to make a recommendation for the OCF for sea ice classification using Cryosat-2 (CS-2) data. Six classifiers including convolutional neural network (CNN), Bayesian, K nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were studied. CS-2 altimeter data of November 2015 and May 2016 in the whole Arctic were used. The overall accuracy was estimated using multivalidation to evaluate the performances of individual classifiers with different feature combinations. Overall, RF achieved a mean accuracy of 89.15%, followed by Bayesian, SVM, and BPNN (~86%), outperforming the worst (CNN and KNN) by 7%. Trailing-edge width (TeW) and leading-edge width (LeW) were the most important features, and feature combination of TeW, LeW, Sigma0, maximum of the returned power waveform (MAX), and pulse peakiness (PP) was the best choice. RF with feature combination of TeW, LeW, Sigma0, MAX, and PP was finally selected as the OCF for sea ice classification and the results that demonstrated this method achieved a mean accuracy of 91.45%, which outperformed the other state-of-art methods by 9%.



A Study on Lunar Regolith Quantitative Random Model and Lunar Penetrating Radar Parameter Inversion

Nov. 2017

Lunar penetrating radar (LPR) is an important way to evaluate the geological structure of the subsurface of the moon. The Chang'E-3 has utilized LPR, which is equipped on the lunar rover named Yutu, to obtain the shallow lunar regolith structure in Mare Imbrium. The previous result provides a unique opportunity to map the subsurface structure and vertical distribution of the lunar regolith with high resolution. In order to evaluate the LPR data, the study of lunar regolith media is of great significance for understanding the material composition of the lunar regolith structure. In this letter, we focus on the lunar regolith quantitative random model and parameter inversion with LPR synthetic data. First, based on the Apollo drilling core data, we build the lunar regolith quantitative random model with clipped Gaussian random field theory. It can be used to model the discrete-valued random field with a given correlation structure. Then, we combine radar wave impedance and stochastic inversion methods to carry out LPR data inversion and parameter estimation. The results mostly provide reliable information on the lunar regolith layer structure and local details with high resolution. This letter presents a further research strategy for lunar probe and deep-space detection with LPR.



Minimum-Based Sliding Window Detectors in Correlated Pareto Distributed Clutter

Nov. 2017

Recent investigations have resulted in the derivation of a multivariate Pareto model, which is consistent with the compound Gaussian model framework, allowing one to describe statistically a correlated Pareto distributed sequence. This has permitted the development of noncoherent sliding window detection processes, for operation in an X-band maritime surveillance radar context, which account for correlated clutter returns. Based upon this multivariate Pareto model, the structure of the sample minimum is investigated, which can then be used to produce decision rules robust to interference. Two such detectors will be examined, and their performance in real high-resolution X-band maritime radar clutter will be investigated. It will be shown that a number of avenues of future work are available.



Hyperspectral Imagery Denoising by Deep Learning With Trainable Nonlinearity Function

Nov. 2017

Hyperspectral images (HSIs) can describe subtle differences in the spectral signatures of objects, and thus they are effective in a wide array of applications. However, an HSI is inevitably contaminated with some unwanted components like noise resulting in spectral distortion, which significantly decreases the performance of postprocessing. In this letter, a deep stage convolutional neural network (CNN) with trainable nonlinearity functions is applied for the first time to remove noise in HSIs. Besides the fact that the weight and bias matrices are learned from cubic training clean-noisy HSI patches, the nonlinearity functions in each stage are also trainable, which differ from the conventional CNN with a fixed nonlinearity function. Compared with the state-of-the-art HSI denoising methods, the experimental results on both synthetic and real HSIs confirm that the proposed method can obtain a more effective and efficient performance.



Dissimilarity-Weighted Sparse Representation for Hyperspectral Image Classification

Nov. 2017

To improve the capability of a traditional sparse representation-based classifier (SRC), we propose a novel dissimilarity-weighted SRC (DWSRC) for hyperspectral image (HSI) classification. In particular, DWSRC computes the weights for each atom according to the distance or dissimilarity information between the test pixel and the atoms. First, a locality constraint dictionary set is constructed by the Gaussian kernel distance with a suitable distance metric (e.g., Euclidean distance). Second, the test pixel is sparsely coded over the new weighted dictionary set based on the 11-norm minimization problem. Finally, the test pixel is classified by using the obtained sparse coefficients with the minimal residual rule. Experimental results on two widely used public HSIs demonstrate that the proposed DWSRC is more efficient and accurate than other state-of-the-art SRCs.



Spectral-Element Method With Divergence-Free Constraint for 2.5-D Marine CSEM Hydrocarbon Exploration

Nov. 2017

Rapid simulations of large-scale low-frequency subsurface electromagnetic measurements are still a challenge because of the low-frequency breakdown phenomenon that makes the system matrix extremely poor-conditioned. Hence, significant attention has been paid to accelerate the numerical algorithms for Maxwell's equations in both integral and partial differential forms. In this letter, we develop a novel 2.5-D method to overcome the low-frequency breakdown problem by using the mixed spectral element method with the divergence-free constraint and apply it to solve the marine-controlled-source electromagnetic systems. By imposing the divergence-free constraint, the proposed method considers the law of conservation of charges, unlike the conventional governing equation for these problems. Therefore, at low frequencies, the Gauss law guarantees the stability of the solution, and we can obtain a well-conditioned system matrix even as the frequency approaches zero. Several numerical experiments show that the proposed method is well suited for solving low-frequency electromagnetic problems.



IAA-Based High-Resolution ISAR Imaging With Small Rotational Angle

Nov. 2017

The Fourier transform-based range Doppler method is commonly used in an inverse synthetic aperture radar. Although it has achieved good success in most scenarios, its performance is determined by the rotational angle, and the cross-range resolution is extremely low in the case of a small rotational angle. In this letter, to improve the cross-range resolution, a novel cross-range compression scheme based on the iterative adaptive approach (IAA) is proposed. In addition to the standard IAA to achieve high resolution, the efficient IAA is introduced to suppress the sidelobes due to noise. Both the simulation and experimental results demonstrate that the proposed method has the advantages of parameter-free, high accuracy, and high efficiency.



Enhanced Target Detection for HFSWR by 2-D MUSIC Based on Sparse Recovery

Nov. 2017

This letter proposes using the 2-D multiple-signal classification (MUSIC) based on sparse recovery (SR) to improve the target-detection capability of high-frequency surface wave radar (HFSWR). Usually, for wide-beam HFSWRs, target detection is first conducted in the range-Doppler spectrum, and bearings are then estimated by superresolution methods such as MUSIC. Unfortunately, the conventional cascaded method can easily result in unfavorable deterioration of multitarget detection when different target signals tend to become mixed in the Doppler spectrum. Moreover, sea clutter is an unwanted signal that frequently masks target signals. To enhance the detection of multiple targets and targets embedded in sea clutter, spatial-temporal joint estimation has been proposed. However, because of the lack of spatial-temporal snapshots caused by the nonstationarity of target signals, the efficiency of the estimator cannot be guaranteed. To overcome this shortcoming, multiple-measurement-vector-based SR, which has been used to solve many under-sampling problems in the past ten years, is adopted. Our approach can effectively detect a target embedded in sea clutter as well as multiple adjacent targets and distinguish them from each other. Results obtained using real data with opportunistic targets validate our approach. Therefore, the proposed 2-D SR-MUSIC approach improves target detection and outperforms conventional cascaded methods.



A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values

Nov. 2017

Random Forests variable importance measures are often used to rank variables by their relevance to a classification problem and subsequently reduce the number of model inputs in high-dimensional data sets, thus increasing computational efficiency. However, as a result of the way that training data and predictor variables are randomly selected for use in constructing each tree and splitting each node, it is also well known that if too few trees are generated, variable importance rankings tend to differ between model runs. In this letter, we characterize the effect of the number of trees (ntree) and class separability on the stability of variable importance rankings and develop a systematic approach to define the number of model runs and/or trees required to achieve stability in variable importance measures. Results demonstrate that both a large ntree for a single model run, or averaged values across multiple model runs with fewer trees, are sufficient for achieving stable mean importance values. While the latter is far more computationally efficient, both the methods tend to lead to the same ranking of variables. Moreover, the optimal number of model runs differs depending on the separability of classes. Recommendations are made to users regarding how to determine the number of model runs and/or trees that are required to achieve stable variable importance rankings.



Sparse Bayesian Learning-Based Seismic Denoise by Using Physical Wavelet as Basis Functions

Nov. 2017

Attenuating random noise is a fundamental yet necessary step for subsequent seismic image processing and interpretation. We introduce a sparse Bayesian learning (SBL)-based seismic denoise method by using the physical wavelet as the basis function. The physical wavelet estimated from seismic and well logging data can appropriately describe the characteristics of the seismic data. Thus, it is an appropriate choice of basis function. Moreover, the tradeoff regularization parameter for determining denoise quality can be adaptively estimated according to the updated data misfit and sparseness degree during the iterative process of the SBL algorithm. The motivation behind the denoise method using sparse representations is that seismic signals can be sparsely represented by using several physical wavelets, whereas noise cannot. Both synthetic and real seismic data examples are adopted to demonstrate the effectiveness of the method.



Rural Building Detection in High-Resolution Imagery Based on a Two-Stage CNN Model

Nov. 2017

High-level feature extraction and hierarchical feature representation of image objects with a convolutional neural network (CNN) can overcome the limitations of the traditional building detection models using middle/low-level features extracted from a complex background. Aiming at the drawbacks of manual village location, high cost, and the limited accuracy of building detection in the existing rural building detection models, a two-stage CNN model is proposed in this letter to detect rural buildings in high-resolution imagery. Simulating the hierarchical processing mechanism of human vision, the proposed model is constructed with two CNNs, whose architectures can automatically locate villages and efficiently detect buildings, respectively. This two-stage CNN model effectively reduces the complexity of the background and improves the efficiency of rural building detection. The experiments showed that the proposed model could automatically locate all the villages in the two study areas, achieving a building detection accuracy of 88%. Compared with the existing models, the proposed model was proved to be effective in detecting buildings in rural areas with a complex background.



Fast Spectral Clustering With Anchor Graph for Large Hyperspectral Images

Nov. 2017

The large-scale hyperspectral image (HSI) clustering problem has attracted significant attention in the field of remote sensing. Most traditional graph-based clustering methods still face challenges in the successful application of the large-scale HSI clustering problem mainly due to their high computational complexity. In this letter, we propose a novel approach, called fast spectral clustering with anchor graph (FSCAG), to efficiently deal with the large-scale HSI clustering problem. Specifically, we consider the spectral and spatial properties of HSI in the anchor graph construction. The proposed FSCAG algorithm first constructs anchor graph and then performs spectral analysis on the graph. With this, the computational complexity can be reduced to O(ndm), which is a significant improvement compared to conventional graph-based clustering methods that need at least O(n2d), where n, d, and m are the number of samples, features, and anchors, respectively. Several experiments are conducted to demonstrate the efficiency and effectiveness of the proposed FSCAG algorithm.



On the Effect of Spatially Non-Disjoint Training and Test Samples on Estimated Model Generalization Capabilities in Supervised Classification With Spatial Features

Nov. 2017

In this letter, we establish two sampling schemes to select training and test sets for supervised classification. We do this in order to investigate whether estimated generalization capabilities of learned models can be positively biased from the use of spatial features. Numerous spatial features impose homogeneity constraints on the image data, whereby a spatially connected set of image elements is attributed identical feature values. In addition to a frequent occurrence of intrinsic spatial autocorrelation, this leads to extrinsic spatial autocorrelation with respect to the image data. The first sampling scheme follows a spatially random partitioning into training and test sets. In contrast to that, the second strategy implements a spatially disjoint partitioning, which considers in particular topological constraints that arise from the deployment of spatial features. Experimental results are obtained from multi- and hyperspectral acquisitions over urban environments. They underline that a large share of the differences between estimated generalization capabilities obtained with the spatially disjoint and non-disjoint sampling strategies can be attributed to the use of spatial features, whereby differences increase with an increasing size of the spatial neighborhood considered for computing a spatial feature. This stresses the necessity of a proper spatial sampling scheme for model evaluation to avoid overoptimistic model assessments.



Recent Advances in Synthetic Aperture Radar Remote Sensing—Systems, Data Processing, and Applications

Nov. 2017

This letter closes a special stream consisting of selected papers from the fifth Asia-Pacific Conference on Synthetic Aperture Radar in 2015 (APSAR 2015). The latest research results and outcomes from APSAR 2015, particularly on the synthetic aperture radar (SAR) systems/subsystems design, data processing techniques, and various SAR applications in remote sensing, are summarized and presented. All these results represent the recent advances in SAR remote sensing. Hopefully, this letter can provide some references for SAR researchers/engineers and stimulate the future development of SAR technology for remote sensing.



A Vessel Detection Method Using Compact-Array HF Radar

Nov. 2017

A compact-array high-frequency surface wave radar equipped with two crossed-loop/monopole receiving antennas has been established for vessel detection. Using two compact antennas of the same design, this system can obtain two extremely similar sets of radar range-Doppler spectra over the same period. To detect vessel targets efficiently, the spectra of two antennas are enhanced by performing a principle component analysis. A wavelet-based approach is then applied to suppress clutter and reduce noise. The signal-to-noise ratios and signal-to-clutter ratios of the echoes are thus improved. Finally, an adaptive threshold is used to extract targets. The real radar data detection results are compared with Automatic Identification System data as well as those from the conventional ordered-statistic constant false alarm rate method. The feasibility and the validity of method proposed here are thus demonstrated.



Polarimetric SAR Image Classification Using a Wishart Test Statistic and a Wishart Dissimilarity Measure

Nov. 2017

Land-cover classification in polarimetric synthetic aperture radar images is a vital technique that has been developed for years. The Wishart distribution, which the polarimetric coherence matrix obeys, has been researched to design the well-known Wishart classifier. This model is appropriate for homogeneous scenes, but it usually fails in reality when a category consists of several subcategories or clusters. Therefore, a simple but powerful sample-merging strategy is proposed to generate representative subcenters, based on a dissimilarity measure. In addition, a weighted likelihood-ratio criterion is also proposed to further improve the performance of the Wishart distribution-based classification, based on the Wishart test statistic. Two experiments on EMISAR and UAVSAR data sets confirm that combining the proposed strategies can achieve better results than can the Wishart classifier and the other existing methods.



Video-Based Estimation of Surface Currents Using a Low-Cost Quadcopter

Nov. 2017

Video imagery of surface waves recorded from a small off-the-shelf quadcopter with a self-stabilizing camera gimbal is analyzed to estimate the surface current field. The nadir looking camera acquires a short image sequence, which is geocoded to Universal Transverse Mercator coordinates. The resulting image sequence is used to quantify characteristic parameters (wavelength, period, and direction) of short (0.1-1 m) surface waves in space and time. This opens the opportunity to fit the linear dispersion relation to the data and thus monitor the frequency shift induced by an ambient current. The fitting is performed by applying a spectral energy-based maximization technique in the wavenumber-frequency domain. The current field is compared with measurements acquired by an acoustic Doppler current profiler mounted on a small boat, showing an overall good agreement. The root-mean-square error in current velocity is 0.09 m/s with no bias.



Constrained Band Subset Selection for Hyperspectral Imagery

Nov. 2017

This letter extends the constrained band selection (CBS) technique to constrained band subset selection (CBSS) in a similar manner that constrained energy minimization has been extended to linearly constrained minimum variance. CBSS constrains multiple bands as a band subset as opposed to CBS constraining a single band as a singleton set. To achieve this goal, CBSS requires a strategy to search for an optimal band subset, while CBS does not. In this letter, two new sequential algorithms, referred to as sequential CBSS and successive CBSS, which do not exist in CBS are derived for CBSS to find desired band subsets and to avoid exhaustive search.



Object Detection Using Convolutional Neural Networks in a Coarse-to-Fine Manner

Nov. 2017

Object detection in remote sensing images has long been studied, but it remains challenging due to the diversity of objects and the complexity of backgrounds. In this letter, we propose an object detection method using convolutional neural networks (CNNs) in a coarse-to-fine manner. In the coarse step, coarse candidate regions that may contain objects are proposed. In the fine step, fine candidate regions are cropped from coarse candidate regions, and are classified as objects or backgrounds. We design a concise and efficient framework that can propose fewer candidate regions and extract more discriminative features. The framework consists of two eight-layer CNNs that are well designed and powerful. To use CNNs to detect inshore ships, image samples are required, each of which should contain only one ship. However, the traditional image cropping method cannot generate such samples. To solve this problem, we present an orientation-free image cropping method that can generate trapezium rather than rectangle samples, making inshore ship detection by CNN feasible. Experimental results on Google Earth images demonstrate that the proposed method outperforms existing state-of-the-art methods.



Design and Application of the Distributed Ionospheric Coherent Scatter Radar

Nov. 2017

In this letter, a newly designed distributed coherent scatter radar for localization of ionospheric irregularities is presented. It is composed of a detection network that can detect ionospheric irregularities with the help of a time synchronization module. To achieve a fairly narrow beam with high directive gain, an antenna array is used in this transmitter module. The frequency band is from high frequency (HF) to very HF to detect irregularities at different scales. In addition, the radar uses a universal serial bus to reduce its size, which allows it to be easily moved to different areas. An iterative ray tracing method is also applied to localize the ionospheric irregularities. The results indicate that the radar can effectively track ionospheric irregularity in 3-D space.



Internal Solitary Waves in the Laptev Sea: First Results of Spaceborne SAR Observations

Nov. 2017

The first results of internal solitary wave (ISW) observations over the ice-free Laptev Sea derived from 354 ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired in May-October 2011 are reported. Analysis of the data reveals the key regions of ISW distribution that are primarily found over the outer shelf/slope regions poleward the M2 critical latitude. Most of the ISWs are observed in regions where enhanced tide-induced vertical mixing and heat fluxes have been previously reported. This suggests that spaceborne SAR observations may serve as a tool to infer local mixing hot spots over the ice-free Arctic Ocean.



Clutter Suppression via Hankel Rank Reduction for DFrFT-Based Vibrometry Applied to SAR

Nov. 2017

Hankel rank reduction (HRR) is a method that, by prearranging the data in a Hankel matrix and performing rank reduction via singular value decomposition, suppresses the noise of a time-history vector comprised of the superposition of a finite number of sinusoids. In this letter, the HRR method is studied for performing clutter suppression in synthetic aperture radar (SAR)-based vibrometry. Specifically, three different applications of the HRR method are presented. First, resembling the SAR slow-time signal model, the HRR method is utilized for separating a chirp signal immersed in a sinusoidal clutter. Second, using simulated airborne SAR data with 10 dB of signal-to-clutter ratio, the HRR method is applied to perform target isolation and to improve the results of an SAR-based vibration estimation algorithm. Finally, the vibrometry approach combined with the HRR method is validated using actual airborne SAR data.



Fisher Vectors for PolSAR Image Classification

Nov. 2017

In this letter, we study the application of the Fisher vector (FV) to the problem of pixelwise supervised classification of polarimetric synthetic aperture radar images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real parts of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive an FV from a mixture of real Wishart densities and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging data sets show the effectiveness of the approach.



Unsupervised Band Selection Using Block-Diagonal Sparsity for Hyperspectral Image Classification

Nov. 2017

In order to alleviate the negative effect of curse of dimensionality, band selection is a crucial step for hyperspectral image (HSI) processing. In this letter, we propose a novel unsupervised band selection approach to reduce the dimensionality for hyperspectral imagery. In order to obtain the most representative bands, the correlation matrix computed from the original HSI is used to describe the correlation characteristics among bands, while the block-diagonal structure is measured to segment all bands into a series of subspace. After applying the spectral clustering algorithm, the optimal combination of band is finally selected. To verify the effectiveness and superiority of the proposed band selection method, experiments have been conducted on three widely used real-world hyperspectral data. The results have shown that the proposed method outperforms other methods in HSI classification application.



SAR-Based Vessel Velocity Estimation From Partially Imaged Kelvin Pattern

Nov. 2017

Spaceborne synthetic aperture radar (SAR) can be considered an operational asset for maritime monitoring applications. Well-assessed approaches exist for ship detection, validated in several maritime surveillance systems. However, measuring vessel velocity from detected single-channel SAR images of ships is in general difficult. This letter contributes to this problem by investigating the possibility of retrieving vessel velocity by wake analysis. An original method for velocity estimation is developed for calm sea (Beaufort scale 1-2) and applied over seven X-band SAR images, gathered by COSMO-SkyMed mission over the Gulf of Naples, Italy. The algorithm exploits the well-known relation between the wavelength of the waves composing the Kelvin pattern and the ship velocity. But the proposed approach extends the applicability of the existing wake-based techniques since it foresees evaluation of the wavelength along a generic direction in the Kelvin angle. Promising results have been achieved, which are in good agreement with those of more assessed techniques for ship velocity estimation in SAR images.



Reverse Backprojection Algorithm for the Accurate Generation of SAR Raw Data of Natural Scenes

Nov. 2017

Future synthetic aperture radar (SAR) mission concepts often rely on locally nonlinear (e.g., high orbits and bistatic) surveys or acquisition schemes. The simulation of the raw data of natural scenes as acquired by future systems appears as one powerful tool in order to understand the particularities of these systems and assess the impact of system and propagation errors on their performance. We put forward, in this letter, a new formulation of the reverse backprojection algorithm for the accurate simulation of raw data of natural surfaces. In particular, the algorithm is perfectly suited to accommodate any kind (1-D/2-D) of temporal and spatial variation, e.g., in observation geometry, acquisition strategy, or atmospheric propagation. The algorithm is analyzed with respect to its SAR image formation sibling, and tested under different simulation scenarios. We expect the reverse backprojection algorithm to play a relevant role in the simulation of future geosynchronous and multistatic SAR missions.



Locality Adaptive Discriminant Analysis for Spectral–Spatial Classification of Hyperspectral Images

Nov. 2017

Linear discriminant analysis (LDA) is a popular technique for supervised dimensionality reduction, but with less concern about a local data structure. This makes LDA inapplicable to many real-world situations, such as hyperspectral image (HSI) classification. In this letter, we propose a novel dimensionality reduction algorithm, locality adaptive discriminant analysis (LADA) for HSI classification. The proposed algorithm aims to learn a representative subspace of data, and focuses on the data points with close relationship in spectral and spatial domains. An intuitive motivation is that data points of the same class have similar spectral feature and the data points among spatial neighborhood are usually associated with the same class. Compared with traditional LDA and its variants, LADA is able to adaptively exploit the local manifold structure of data. Experiments carried out on several real hyperspectral data sets demonstrate the effectiveness of the proposed method.



Stereo Image Retrieval Using Height and Planar Visual Word Pairs

Nov. 2017

The wide availability of high-resolution satellite stereo images has created a surging demand for effective stereo image retrieval methods. Recently, few retrieval methods have been designed specifically for stereo images having unique characteristics (e.g., viewing number and viewing angles), and often have insufficient retrieval accuracy. A new content-based stereo image retrieval method is achieved with height and planar visual word pairs, which are generated from the stereo extracted digital surface models and orthoimages. Experimental results of the International Society for Photogrammetry and Remote Sensing stereo benchmark test data set show that our method outperforms the state-of-the-art methods in terms of accuracy and stability. Our method achieves a high retrieval precision of 0.9, and has a high efficiency. Our method is stable for two stereo pairs, covering the same scene from different sensors, which usually have a small ranking difference in the returned ranking list. Our method is helpful to quickly and accurately locate desired stereo images from large quantities of multisensor stereo images.



Improved ISAC Algorithm to Retrieve Atmospheric Parameters From HyTES Hyperspectral Images Using Gaussian Mixture Model and Error Ellipse

Nov. 2017

In-scene atmospheric correction (ISAC) is a procedure that accounts for atmospheric effects by direct use of the hyperspectral radiance data without recourse to ancillary meteorological data. This letter aims to improve the accuracy of the ISAC algorithm. In the ISAC method after calculating brightness temperature, the computed radiance and measured radiance at the sensor are plotted on a graph in each band. Then, to estimate atmospheric parameters, the straight line is fit to the upper boundary of the plot. One of the issues in ISAC is to find an optimal upper boundary of data. The main innovation of this letter is the use of Gaussian mixture model (GMM) and error ellipse to find the optimal upper boundary of data and fit the line to it. In the line fitting process, first, a GMM with the optimum class number derived by Akaike information criterion (AIC) is implemented on thermal hyperspectral data and then, the optimal upper boundary is selected for each class and a straight line is fit to it. Finally, the desired parameters are obtained with weighted linear combination of the results from all classes. For quality assessment, the results were compared with atmospheric products of Hyperspectral Thermal Emission Spectrometer sensor and atmospheric parameters that were obtained from traditional ISAC. Root-mean-square errors for atmospheric transmittance obtained from GMM for bands 9.8 and 11.5 μm are 0.0008 and 0.0106 and those for upwelling atmospheric radiance are 0.675 and 0.0265, respectively.



MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

Nov. 2017

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks. However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model G and a discriminative model D. We treat D as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. G can produce numerous images that are similar to the training data; therefore, D can learn better representations of remotely sensed images using the training data provided by G. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.



SAR Target Discrimination Based on BOW Model With Sample-Reweighted Category-Specific and Shared Dictionary Learning

Nov. 2017

To improve the synthetic aperture radar (SAR) target discrimination performance under complex scenes, this letter presents a new SAR target discrimination method based on the bag-of-words model. The method contains three main stages. In the local feature extraction stage, the SAR-SIFT feature is extracted. In the feature coding stage, we improve the existing category-specific and shared dictionary learning (CSDL) and propose the sample-reweighted CSDL (SR-CSDL). The local features are sparsely coded using the codebook learned from SR-CSDL. In the feature pooling stage, spatial pyramid matching with max pooling is used to aggregate the local coding coefficients to generate the global feature for each chip image. Experimental results using the miniSAR data verify the effectiveness of the proposed method.



Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification

Nov. 2017

This letter proposes a novel multiview feature extraction method for supervised polarimetric synthetic aperture radar (PolSAR) image classification. PolSAR images can be characterized by multiview feature sets, such as polarimetric features and textural features. Canonical correlation analysis (CCA) is a well-known dimensionality reduction (DR) method to extract valuable information from multiview feature sets. However, it cannot exploit the discriminative information, which influences its performance of classification. Local discriminant embedding (LDE) is a supervised DR method, which can preserve the discriminative information and the local structure of the data well. However, it is a single-view learning method, which does not consider the relation between multiple view feature sets. Therefore, we propose local discriminant CCA by incorporating the idea of LDE into CCA. Specific to PolSAR images, a symmetric version of revised Wishart distance is used to construct the between-class and within-class neighboring graphs. Then, by maximizing the correlation of neighboring samples from the same class and minimizing the correlation of neighboring samples from different classes, we find two projection matrices to achieve feature extraction. Experimental results on the real PolSAR data sets demonstrate the effectiveness of the proposed method.



Spatial Downscaling of SMAP Soil Moisture Using MODIS Land Surface Temperature and NDVI During SMAPVEX15

Nov. 2017

The Soil Moisture Active Passive (SMAP) mission provides a global surface soil moisture (SM) product at 36-km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications, there are also a lot of applications which would benefit from a higher resolution SM product. The SMAP radiometer-based SM product was downscaled to 1 km using Moderate Resolution Imaging Spectroradiometer (MODIS) data and validated against airborne data from the Passive Active L-band System instrument. The downscaling approach uses MODIS land surface temperature and normalized difference vegetation index to construct soil evaporative efficiency, which is used to downscale the SMAP SM. The algorithm was applied to one SMAP pixel during the SMAP Validation Experiment 2015 (SMAPVEX15) in a semiarid study area for validation of the approach. SMAPVEX15 offers a unique data set for testing SM downscaling algorithms. The results indicated reasonable skill (root-mean-square difference of 0.053 m3/m3 for 1-km resolution and 0.037 m3/m3 for 3-km resolution) in resolving high-resolution SM features within the coarse-scale pixel. The success benefits from the fact that the surface temperature in this region is controlled by soil evaporation, the topographical variation within the chosen pixel area is relatively moderate, and the vegetation density is relatively low over most parts of the pixel. The analysis showed that the combination of the SMAP and MODIS data under these conditions can result in a high-resolution SM product with an accuracy suitable for many applications.



A New Unsupervised Hyperspectral Band Selection Method Based on Multiobjective Optimization

Nov. 2017

Unsupervised band selection methods usually assume specific optimization objectives, which may include band or spatial relationship. However, since one objective could only represent parts of hyperspectral characteristics, it is difficult to determine which objective is the most appropriate. In this letter, we propose a new multiobjective optimization-based band selection method, which is able to simultaneously optimize several objectives. The hyperspectral band selection is transformed into a combinational optimization problem, where each band is represented by a binary code. More importantly, to overcome the problem of unique solution selection in traditional multiobjective methods, we develop a new incorporated rank-based solution set concentration approach in the process of Tchebycheff decomposition. The performance of our method is evaluated under the application of hyperspectral imagery classification. Three recently proposed band selection methods are compared.



Hyperspectral Image Classification via Low-Rank and Sparse Representation With Spectral Consistency Constraint

Nov. 2017

In this letter, a low-rank and sparse representation classifier with a spectral consistency constraint (LRSRC-SCC) is proposed. Different from the SRC that represents samples individually, LRSRC-SCC reconstructs samples jointly and is able to capture the local and global structures simultaneously. In this proposed classifier, an adaptive spectral constraint is imposed on both the low-rank and sparse terms so as to better reveal the data structure and enhance its discriminative power. In addition, the alternating direction method is introduced to solve the underlying minimization problem, in which, more importantly, the subobjective function associated with the low-rank term is optimized based on the rank equivalence between a matrix and its Gram matrix, resulting in a closed-form solution. Finally, LRSRC-SCC is extended to LRSRC-SCCE for fully exploiting the spatial information. Experimental results on two hyperspectral data sets demonstrate that the proposed LRSRC-SCC and LRSRC-SCCE methods outperform some state-of-the-art methods.



User-Friendly InSAR Data Products: Fast and Simple Timeseries Processing

Nov. 2017

Interferometric synthetic aperture radar (InSAR) methods provide high-resolution maps of surface deformation applicable to many scientific, engineering, and management studies. Despite its utility, the specialized skills and computer resources required for InSAR analysis remain as barriers for truly widespread use of the technique. Reduction of radar scenes to maps of temporal deformation evolution requires not only detailed metadata describing the exact radar and surface acquisition geometries, but also a software package that can combine these for the specific scenes of interest. Furthermore, the range-Doppler reference frame and radar coordinate system itself are confusing, so that many users find it hard to incorporate even useful products in their customary analyses. Finally, the sheer data volume needed for interferogram time series makes InSAR analysis challenging for many analysis systems. We show here that it is possible to deliver radar data products to users that address all of these difficulties, so that the data acquired by large, modern satellite systems are ready to use in more natural coordinates, without requiring further processing, and in as small volume as possible.



A Novel Deep Embedding Network for Building Shape Recognition

Nov. 2017

Building shape, as a key structured element, plays a significant role in various urban remote sensing applications. However, because of high complexity and intraclass variations between building structures, the capability of building shape description and recognition becomes limited or even impoverished. In this letter, a novel deep embedding network is proposed for building shape recognition, which combines the strength of the unsupervised feature learning of convolutional neural networks (CNNs) and a novel triplet loss. Specifically, we take advantage of the strong discriminative power of CNNs to learn an efficient building shape representation for shape recognition. With this deep embedding network, the high-dimensional image space can be mapped into a low-dimensional feature space, and the deep features can effectively reduce the intraclass variations while increasing the interclass variation between different building shape images. Afterward, the derived deep features are exploited for the process of building shape recognition. This method consists of two stages. In the first stage, for standard building shape image queries stored in the shape primitives library and the building shape data set, two sets of deep features are extracted with the deep embedding network. In the second stage, we formulate the shape recognition task into a feature matching problem and the final building shape recognition results can be achieved by set-to-set feature matching method. Experiments on the VHR-10 and UCML data sets demonstrate the effectiveness and precision of the proposed method.



Understanding the Heterogeneity of Soil Moisture and Evapotranspiration Using Multiscale Observations From Satellites, Airborne Sensors, and a Ground-Based Observation Matrix

Nov. 2017

This letter summarizes a special stream of the IEEE Geoscience and Remote Sensing Letters devoted to understanding the heterogeneity in soil moisture, evapotranspiration, and other related ecohydrological variables based on multiscale observations from satellite-based and airborne remote sensors, a flux observation matrix, and an ecohydrological wireless sensor network in the Heihe Watershed Allied Telemetry Experimental Research project. Scaling and uncertainty are the key issues in the remote-sensing research community, especially regarding the heterogeneous land surface. However, a lack of understanding and an inadequate theoretical basis impede the development and innovation of forward radiative transfer models, as well as the quantitative retrieval and validation of remote-sensing products. We summarize the prior considerations regarding surface heterogeneity research and report the main outcomes and contributions of this special stream. The highlights of this stream are related to spatial sampling, upscaling, uncertainty analysis, the validation of remote-sensing products, and accounting for heterogeneity in remote-sensing models.



SRTM DEM-Aided Mapping Satellite-1 Image Geopositioning Without Ground Control Points

Nov. 2017

A Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM)-aided geopositioning method is proposed to solve the problem of geopositioning without ground control points for Mapping Satellite-1 imagery. The method comprises coarse and accurate correction stages, and it compensates errors gradually. DEM extraction and DEM matching are important steps in both the stages, the objectives of which are to compensate the relative and absolute errors in an image, respectively. The SRTM DEM is integrated into all the processes to take full advantage of its consistent and high accuracy. Experimental results showed that this method could greatly improve geometry accuracy and obtain stable and highly accurate geopositioning for Mapping Satellite-1 images, regardless of the land area proportion (LAP) or the production mode. The planimetric and vertical accuracies were better than 8.1 and 5.2 m, respectively, which could satisfy the accuracy requirements of mapping at 1:50 000 scale. The computational efficiency depends on the LAP and target DEM resolution.



Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification

Nov. 2017

This letter introduces a new spectral-spatial classification method for hyperspectral images. A multiscale superpixel segmentation is first used to model the distribution of classes based on spatial information. In this context, the original hyperspectral image is integrated with segmentation maps via a feature fusion process in different scales such that the pixel-level data can be represented by multiscale superpixel-level (MSP) data sets. Then, a subspace-based support vector machine (SVMsub) is adopted to obtain the classification maps with multiscale inputs. Finally, the classification result is achieved via a decision fusion process. The resulting method, called MSP-SVMsub, makes use of the spatial and spectral coherences, and contributes to better feature characterization. Experimental results based on two real hyperspectral data sets indicate that the MSP-SVMsub exhibits good performance compared with other related methods.



Hyperspectral Band Selection Using Improved Classification Map

Nov. 2017

Although it is a powerful feature selection algorithm, the wrapper method is rarely used for hyperspectral band selection. Its accuracy is restricted by the number of labeled training samples and collecting such label information for hyperspectral image is time consuming and expensive. Benefited from the local smoothness of hyperspectral images, a simple yet effective semisupervised wrapper method is proposed, where the edge preserved filtering is exploited to improve the pixel-wised classification map and this in turn can be used to assess the quality of band set. The property of the proposed method lies in using the information of abundant unlabeled samples and valued labeled samples simultaneously. The effectiveness of the proposed method is illustrated with five real hyperspectral data sets. Compared with other wrapper methods, the proposed method shows consistently better performance.



Hyperspectral Pansharpening With Guided Filter

Nov. 2017

A new hyperspectral (HS) pansharpening method based on guided filter is proposed in this letter. The proposed method, which obtains the spatial detail difference of each band successively, is different from the traditional component substitution method. The detail information of each band is extracted at first. Then, the panchromatic (PAN) image is sharpened to enhance the details. The spatial information difference between the enhanced PAN image and the detail information of each band is obtained using the guided filter, without causing spectral and spatial distortion. In order to reduce spectral distortion and add enough spatial information, the injection gains matrix is generated. The fused HS image is finally achieved by injecting the corresponding spatial difference into each band of the interpolated HS image. Experiments demonstrate that the proposed method can obtain superior performance in terms of subjective and objective evaluations.



A Robust Yaw and Pitch Estimation Method for Mini-InSAR System

Nov. 2017

For the mini-interferometric synthetic aperture radar system mounted on small aircraft or unmanned aerial vehicles, yaw and pitch angle deviations can be considerably high due to their small size and atmospheric turbulence. Moreover, we cannot install a large-volume, heavy-weight, and high-cost inertial navigation system limited by the aircraft's carrying capacity and system cost. In view of the problem, this letter proposes a robust yaw and pitch angle estimation method based on the relationship between range-variant Doppler centroid and attitude angles. For each azimuth moment, estimate the range-variant Doppler centroid for each range gate and solve the range-variant Doppler centroid model using a total least squares method to obtain a robust yaw and pitch angle estimation result. The comparison of the estimated and recorded yaw and pitch angles by a high-accuracy position and orientation system validated the effectiveness and reliability of our proposed yaw and pitch angle estimation method.



Ionospheric Decontamination for HF Hybrid Sky-Surface Wave Radar on a Shipborne Platform

Nov. 2017

This letter describes a method of correcting ionospheric frequency modulation for a high-frequency hybrid sky-surface wave radar mounted on a shipborne platform. In the proposed method, azimuth-dependent sea clutter signals are first decomposed into monocomponent signals based on distinguishable differences in their directions of incidence. Afterward, based on the decomposed monocomponent signals, the statistical mean of the time derivatives of the signal phases, weighted by the signal amplitudes, is used to estimate the ionospheric frequency modulation. Finally, the estimated result is applied to the received data to compensate for the ionospheric contamination. Numerical results on simulated data demonstrate the effectiveness of the proposed algorithm.



Weight-Based Rotation Forest for Hyperspectral Image Classification

Nov. 2017

In this letter, we propose a new weight-based rotation forest (WRoF) induction algorithm for the classification of hyperspectral image. The main idea of the new method is to guide the growth of trees adaptively via exploring the potential of important instances. The importance of a training instance is reflected by a dynamic weight function. The higher the weight of an instance, the more the next tree will have to focus on the instance. Experimental results on two real hyperspectral data sets show that the WRoF algorithm results in significant classification improvement compared with random forests and rotation forest.



IEEE Geoscience and Remote Sensing Letters information for authors

Nov. 2017

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.



IEEE Geoscience and Remote Sensing Letters Institutional Listings

Nov. 2017

Presents the GRSS society institutional listings.