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

IEEE Geoscience and Remote Sensing Letters - new TOC



TOC Alert for Publication# 8859



 



[Front cover/Table of contents]

Feb. 2018

Presents the cover/table of contents for this issue of the periodical.



IEEE Geoscience and Remote Sensing Letters publication information

Feb. 2018

Provides a listing of current staff, committee members and society officers.



Table of contents

Feb. 2018

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



Atmospheric Humidity Sounding Using Differential Absorption Radar Near 183 GHz

Feb. 2018

A tunable G-band frequency-modulated continuous-wave radar system has been developed and used to perform differential absorption atmospheric humidity measurements for the first time. The radar's transmitter uses high- power-handling GaAs Schottky diodes to generate between 15-23 dBm over a 10-GHz bandwidth near 183 GHz. By virtue of a high-isolation circular polarization duplexer, the monostatic radar's receiver maintains a noise figure of about 7 dB even while the transmitter is on. With an antenna gain of 40 dB, high-SNR detection of light rain is achieved out to several hundred meters distance. Owing to the strong spectral dependence of the atmospheric absorption over the upper flank of the 183-GHz water absorption line, range-resolved measurements of absolute humidity can be obtained by ratioing the rain echoes over both range and frequency. Absorption measurements obtained are consistent with models of atmospheric millimeter-wave attenuation, and they demonstrate a new method for improving the accuracy of humidity measurements inside of clouds.



Object Tracking in Satellite Videos by Fusing the Kernel Correlation Filter and the Three-Frame-Difference Algorithm

Feb. 2018

Object tracking is a popular topic in the field of computer vision. The detailed spatial information provided by a very high resolution remote sensing sensor makes it possible to track targets of interest in satellite videos. In recent years, correlation filters have yielded promising results. However, in terms of dealing with object tracking in satellite videos, the kernel correlation filter (KCF) tracker achieves poor results due to the fact that the size of each target is too small compared with the entire image, and the target and the background are very similar. Therefore, in this letter, we propose a new object tracking method for satellite videos by fusing the KCF tracker and a three-frame-difference algorithm. A specific strategy is proposed herein for taking advantage of the KCF tracker and the three-frame-difference algorithm to build a strong tracker. We evaluate the proposed method in three satellite videos and show its superiority to other state-of-the-art tracking methods.



Semantic Segmentation of Aerial Images With Shuffling Convolutional Neural Networks

Feb. 2018

Semantic segmentation of aerial images refers to assigning one land cover category to each pixel. This is a challenging task due to the great differences in the appearances of ground objects. Many attempts have been made during the past decades. In recent years, convolutional neural networks (CNNs) have been introduced in the remote sensing field, and various solutions have been proposed to realize dense semantic labeling with CNNs. In this letter, we propose shuffling CNNs to realize semantic segmentation of aerial images in a periodic shuffling manner. This approach is a supplement to current methods for semantic segmentation of aerial images. We propose a naive version and a deeper version of this method, and both are adept at detecting small objects. Additionally, we propose a method called field-of-view (FoV) enhancement that can enhance the predictions. This method can be applied to various networks, and our experiments verify its effectiveness. The final results are further improved through an ensemble method that averages the score maps generated by the models at different checkpoints of the same network. We evaluate our models using the ISPRS Vaihingen and Potsdam data sets, and we acquire promising results using these two data sets.



Improving TMPA 3B43 V7 Data Sets Using Land-Surface Characteristics and Ground Observations on the Qinghai–Tibet Plateau

Feb. 2018

The accurate knowledge of precipitation information over the Qinghai-Tibet Plateau, where the rain gauge networks are limited, is vital for various applications. While satellite-based precipitation estimates provide high spatial resolution (0.25°), large uncertainties and systematic anomalies still exist over this critical area. To derive more accurate monthly precipitation estimates, a spatial data-mining algorithm was used to remove the obvious anomalies compared with their neighbors from the original Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) 3B43 V7 data at an annual scale, as the TMPA data are more accurate than other satellite-based precipitation estimates. To supplement the international exchange stations, additional ground observations were used to calibrate and improve the TMPA data with anomalies removed at an annual scale. Finally, a disaggregation strategy was adopted to derive monthly precipitation estimates based on the calibrated TMPA data. We concluded that: 1) the obvious anomalies compared with their neighbors could be removed from the original TMPA data sets and 2) the calibrated results were of a higher quality than the original TMPA data in each month from 2000 to 2013. The improved TMPA 3B43 V7 data sets over the Qinghai-Tibet plateau, named NITMPA3B43_QTP, are available at http://agri.zju.edu.cn/NITMPA3B43_QTP/.



Scene Classification Based on Two-Stage Deep Feature Fusion

Feb. 2018

In convolutional neural networks (CNNs), higher layer information is more abstract and more task specific, so people usually concern themselves with fully connected (FC) layer features, believing that lower layer features are less discriminative. However, a few researchers showed that the lower layers also provide very rich and powerful information for image representation. In view of these study findings, in this letter, we attempt to adaptively and explicitly combine the activations from intermediate and FC layers to generate a new CNN with directed acyclic graph topology, which is called the converted CNN. After that, two converted CNNs are integrated together to further improve the classification performance. We validate our proposed two-stage deep feature fusion model over two publicly available remote sensing data sets, and achieve a state-of-the-art performance in scene classification tasks.



Spectral Unmixing With Multiple Dictionaries

Feb. 2018

Spectral unmixing aims at recovering the spectral signatures of materials, called endmembers, mixed in a hyperspectral image (HSI) or multispectral image, along with their abundances. A typical assumption is that the image contains one pure pixel per endmember, in which case spectral unmixing reduces to identifying these pixels. Many fully automated methods have been proposed in recent years, but little work has been done to allow users to select areas where pure pixels are present manually or using a segmentation algorithm. Additionally, in a nonblind approach, several spectral libraries may be available rather than a single one, with a fixed number (or an upper or lower bound) of endmembers to chose from each. In this letter, we propose a multiple-dictionary constrained low-rank matrix approximation model that addresses these two problems. We propose an algorithm to compute this model, dubbed multiple matching pursuit alternating least squares, and its performance is discussed on both synthetic and real HSIs.



Development and Assessment of a Data Set Containing Frame Images and Dense Airborne Laser Scanning Point Clouds

Feb. 2018

This letter describes the main features of a data set that contains aerial images acquired with a medium format digital camera and point clouds collected using an airborne laser scanning unit, as well as ground control points and direct georeferencing data. The flights were performed in 2014 over an urban area in Presidente Prudente, São Paulo, Brazil, using different flight heights. These flights covered several features of interest for research, including buildings of different sizes and roof materials, roads, and vegetation. Three point clouds with different densities, a block of digital aerial images, and auxiliary data are available. A geometric assessment was conducted to ensure the accuracy and consistency of the data, and an RMSE of 7 cm was achieved using bundle block adjustment. The data set is freely available for download, and it will be expanded with data collected over time.



One-Dimensional Mirrored Aperture Synthesis With Rotating Reflector

Feb. 2018

In this letter, 1-D mirrored aperture synthesis with a rotating reflector (1-D MAS-R) is proposed to improve the spatial resolution and reduce the number of required antennas for passive microwave remote sensing. The principle of the 1-D MAS-R with an antenna array is given, and from the principle, the 1-D MAS-R with only one antenna can also reconstruct the image of the scene. Simulation results demonstrate the validity of the 1-D MAS-R even with only one antenna, and the spatial resolution is improved by increasing the distance between the reflector and the antenna or antenna array.



Mapping the Spatiotemporal Dynamics of Europe’s Land Surface Temperatures

Feb. 2018

The land surface temperature (LST) drives many terrestrial biophysical processes and varies rapidly in space and time primarily due to the earth's diurnal and annual cycles. Models of the diurnal and annual LST cycle retrieved from satellite data can be reduced to several gap-free parameters that represent the surface's thermal characteristics and provide a generalized characterization of the LST temporal dynamics. In this letter, we use such an approach to map Europe's annual and diurnal LST dynamics. In particular, we reduce a five-year time series (2009-2013) of diurnal LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to 48 sets of half-hourly annual cycle parameters (ACPs), namely, the mean annual LST, the yearly amplitude of LST, and the LST phase shift from the spring equinox. The derived data provide a complete representation of how mainland Europe responds to the heating of the sun and the nighttime LST decay and reveal how Europe's biogeographic regions differ in that respect. We further argue that the SEVIRI ACP can provide an observation-based spatially consistent background for studying and characterizing the thermal behavior of the surface and also a data set to support climate classification at a finer spatial resolution.



A CFCC-LSTM Model for Sea Surface Temperature Prediction

Feb. 2018

Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.



Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks

Feb. 2018

Because the collection of ground-truth labels is difficult, expensive, and time-consuming, classifying hyperspectral images (HSIs) with few training samples is a challenging problem. In this letter, we propose a novel semisupervised algorithm for the classification of hyperspectral data by training a customized generative adversarial network (GAN) for hyperspectral data. The GAN constructs an adversarial game between a discriminator and a generator. The generator generates samples that are not distinguishable by the discriminator, and the discriminator determines whether or not a sample is composed of real data. We design a semisupervised framework for HSI data based on a 1-D GAN (HSGAN). This framework enables the automatic extraction of spectral features for HSI classification. When HSGAN is trained using unlabeled hyperspectral data, the generator can generate hyperspectral samples that are similar to the real data, while the discriminator contains the features, which can be used to classify hyperspectral data with only a small number of labeled samples. The performance of the HSGAN is evaluated on the Airborne Visible Infrared Imaging Spectrometer image data, and the results show that the proposed framework achieves very promising results with a small number of labeled samples.



Moving Point Target Detection Based on Higher Order Statistics in Very Low SNR

Feb. 2018

This letter presents an approach for the detection of moving point targets on high-frame-rate image sequences with low spatial resolution and low SNR based on higher order statistical theory. We propose a novel method for analyzing the time-domain evolution of image data for distinguishing between the background and the target in situations when the spatial signal of the target is swamped by noise. Our method is formulated to detect a time-domain transient signal of unknown scale and arrival time in noisy background. We proposed a bispectrum-based model to characterize the temporal behavior of pixels, and the detection ability under different frame rates and SNRs is analyzed. The method is evaluated using both simulated and real-world data, and we provide a comparison to other widely used point target detection approaches. Our experimental results demonstrate that our algorithm can efficiently detect extremely low SNR targets that are virtually invisible to humans based on time-domain analysis of image sequences.



Noise Performance Comparison Between Continuous Wave and Stroboscopic Pulse Ground Penetrating Radar

Feb. 2018

Although stroboscopic pulse (SP) ground penetrating radar (GPR) is the most popular and widespread equipment for subsoil investigation, continuous-wave (CW) radar has better performance in terms of noise, system dynamic range, and penetration depth, at the expense of greater complexity and cost of the components. The aim of this letter is a direct comparison between SP GPR and CW GPR through an extensive measurement campaign in five different locations representative of the different conditions where a GPR could operate.



Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features

Feb. 2018

In this letter, we propose an empirical-mode decomposition (EMD)-based method for automatic multicategory mini-unmanned aerial vehicle (UAV) classification. The radar echo signal is first decomposed into a set of oscillating waveforms by EMD. Then, eight statistical and geometrical features are extracted from the oscillating waveforms to capture the phenomenon of blade flashes. After feature normalization and fusion, a nonlinear support vector machine is trained for target class-label prediction. Our empirical results on real measurement of radar signals show encouraging mini-UAV classification accuracy performance.



Remote Sensing Image Registration Using Convolutional Neural Network Features

Feb. 2018

Successful remote sensing image registration is an important step for many remote sensing applications. The scale-invariant feature transform (SIFT) is a well-known method for remote sensing image registration, with many variants of SIFT proposed. However, it only uses local low-level information, and loses much middle- or high-level information to register. Image features extracted by a convolutional neural network (CNN) have achieved the state-of-the-art performance for image classification and retrieval problems, and can provide much middle- and high-level information for remote sensing image registration. Hence, in this letter, we investigate how to calculate the CNN feature, and study the way to fuse SIFT and CNN features for remote sensing image registration. The experimental results demonstrate that the proposed method yields a better registration performance in terms of both the aligning accuracy and the number of correct correspondences.



Quantification of the Relationship Between Sea Surface Roughness and the Size of the Glistening Zone for GNSS-R

Feb. 2018

A formulation of the relationship between sea-surface roughness and extension of the glistening zone (GZ) of a Global Navigation Satellite System Reflectometry (GNSS-R) system is presented. First, an analytical expression of the link between GZ area, viewing geometry, and surface mean square slope (MSS) is derived. Then, a strategy for retrieval of surface roughness from the delay-Doppler map (DDM) is illustrated, including details of data preprocessing, quality control, and GZ area estimation from the DDM. Next, an example for application of the proposed approach to spaceborne GNSS-R remote sensing is provided, using DDMs from the TechDemoSat-1 mission. The algorithm is first calibrated using collocated in situ roughness estimates using data sets from the National Data Buoy Center, its retrieval performance is then assessed, and some of the limitations of the suggested technique are discussed. Overall, good correlation is found between buoy-derived MSS and estimates obtained using the proposed strategy ($r=0.73$ ).



PSOSAC: Particle Swarm Optimization Sample Consensus Algorithm for Remote Sensing Image Registration

Feb. 2018

Image registration is an important preprocessing step for many remote sensing image processing applications, and its result will affect the performance of the follow-up procedures. Establishing reliable matches is a key issue in point matching-based image registration. Due to the significant intensity mapping difference between remote sensing images, it may be difficult to find enough correct matches from the tentative matches. In this letter, particle swarm optimization (PSO) sample consensus algorithm is proposed for remote sensing image registration. Different from random sample consensus (RANSAC) algorithm, the proposed method directly samples the modal transformation parameter rather than randomly selecting tentative matches. Thus, the proposed method is less sensitive to the correct rate than RANSAC, and it has the ability to handle lower correct rate and more matches. Meanwhile, PSO is utilized to optimize parameter as its efficiency. The proposed method is tested on several multisensor remote sensing image pairs. The experimental results indicate that the proposed method yields a better registration performance in terms of both the number of correct matches and aligning accuracy.



Localization of Multiple Underwater Objects With Gravity Field and Gravity Gradient Tensor

Feb. 2018

We present a novel algorithm to locate multiple underwater objects in real time using gravity field vector and gravity gradient tensor signals. This algorithm formulates the task of localization of multiple underwater objects into a regularized nonlinear problem, which is solved with the standard Levenberg–Marquardt algorithm. The regularization parameters are estimated by cross validation. The initial coordinates and masses of these underwater objects are automatically determined by solving a single-object localization problem. A synthetic navigation model with two underwater objects was adopted to validate the proposed algorithm. The results show that it has good stability and antinoise ability for multiple underwater objects localizations.



Optimal and Suboptimal Velocity Estimators for ArcSAR With Distributed Target

Feb. 2018

Two new methods of radial velocity estimation for distributed targets in arc-scanning synthetic aperture radar (ArcSAR) systems, namely, the maximum-likelihood estimator (MLE) and the suboptimal method based on the least squares estimation (LSE), are proposed, derived, and analyzed. To this end, we establish that $n$ scatterers of the distributed target are uniformly dispersed within the radar resolution cell of dimensions $a times b$ and they move randomly at different velocities. Furthermore, the effect of the antenna pattern is considered to characterize the amplitude of the scattered signal. Thus, from the coherent integration of the scatters at each pulse repetition interval in radar scanning, $m$ data sequences are obtained as samples of the composite signal, which follows a multivariate normal distribution. From this, the covariance matrix, upon which the methods are based, is derived. Simulations have been carried out to compare the new methods with existing methods, namely, phase, energy, and correlation, as a function of the signal-to-noise ratio. Finally, the results show that the MLE and LSE methods outperform the conventional methods, providing a gain of more than 10 dB.



DropBand: A Simple and Effective Method for Promoting the Scene Classification Accuracy of Convolutional Neural Networks for VHR Remote Sensing Imagery

Feb. 2018

The dropout and data augmentation techniques are widely used to prevent a convolutional neural network (CNN) from overfitting. However, the dropout technique does not work well when applied to the input channels of neural networks, and data augmentation is usually employed along the image plane. In this letter, we present DropBand, which is a simple and effective method of promoting the classification accuracy of CNNs for very-high-resolution remote sensing image scenes. In DropBand, more training samples are generated by dropping certain spectral bands out of original images. Furthermore, all samples with the same set of spectral bands are collected together to train a base CNN. The final prediction for a test sample is represented by the combination of outputs of all base CNNs. The experimental results for three publicly available data sets, i.e., the SAT-4, SAT-6, and UC-Merced image data sets, show that DropBand can significantly improve the classification accuracy of a CNN.



Integration of Contextual Knowledge in Unsupervised Subpixel Classification: Semivariogram and Pixel-Affinity Based Approaches

Feb. 2018

This letter investigates the use of coarse-image features for predicting class labels at a given finer spatial scale. In this regard, two unsupervised subpixel mapping approaches, a semivariogram method, and a pixel-affinity based method are proposed. Furthermore, segmentation-based spectral unmixing is explored so as to address the spectral variability and nonconvexity of classes. In addition, the gradient information is employed to resolve uncertainties in the unmixing process. The proposed modifications based on pixel-affinity and semivariogram have produced an accuracy improvement of 5% or more over the state-of-the-art approaches.



Spectral Clustering of Straight-Line Segments for Roof Plane Extraction From Airborne LiDAR Point Clouds

Feb. 2018

This letter presents a novel approach to automated extraction of roof planes from airborne light detection and ranging data based on spectral clustering of straight-line segments. The straight-line segments are derived from laser scan lines, and 3-D line geometry analysis is employed to identify coplanar line segments so as to avoid skew lines in plane estimation. Spectral analysis reveals the spectrum of the adjacency matrix formed by the straight-line segments. Spectral clustering is then performed in feature space where the clusters are more prominent, resulting in a more robust extraction of roof planes. The proposed approach has been tested on ISPRS benchmark data sets, with the results showing high quality in terms of completeness, correctness, and geometrical accuracy, thus confirming that the proposed approach can extract roof planes both accurately and efficiently.



Seismic Waveform Classification and First-Break Picking Using Convolution Neural Networks

Feb. 2018

Regardless of successful applications of the convolutional neural networks (CNNs) in different fields, its application to seismic waveform classification and first-break (FB) picking has not been explored yet. This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave. We use representative subimage samples with two types of labeled waveform classification to supervise CNNs training. The goal is to obtain the optimal weights and biases in CNNs, which are solved by minimizing the error between predicted and target label classification. The trained CNNs can be utilized to automatically extract a set of time-space attributes or features from any subimage in shot gathers. These attributes are subsequently inputted to the trained fully connected layer of CNNs to output two values between 0 and 1. Based on the two-element outputs, a discriminant score function is defined to provide a single indication for classifying input waveforms. The FB is then located from the calculated score maps by sequentially using a threshold, the first local minimum rule of every trace and a median filter. Finally, we adopt synthetic and real shot data examples to demonstrate the effectiveness of CNNs-based waveform classification and FB picking. The results illustrate that CNN is an efficient automatic data-driven classifier and picker.



Modified Tensor Locality Preserving Projection for Dimensionality Reduction of Hyperspectral Images

Feb. 2018

By considering the cubic nature of hyperspectral image (HSI) to address the issue of the curse of dimensionality, we have introduced a tensor locality preserving projection (TLPP) algorithm for HSI dimensionality reduction and classification. The TLPP algorithm reveals the local structure of the original data through constructing an adjacency graph. However, the hyperspectral data are often susceptible to noise, which may lead to inaccurate graph construction. To resolve this issue, we propose a modified TLPP (MTLPP) via building an adjacency graph on a dual feature space rather than the original space. To this end, the region covariance descriptor is exploited to characterize a region of interest around each hyperspectral pixel. The resulting covariances are the symmetric positive definite matrices lying on a Riemannian manifold such that the Log-Euclidean metric is utilized as the similarity measure for the search of the nearest neighbors. Since the defined covariance feature is more robust against noise, the constructed graph can preserve the intrinsic geometric structure of data and enhance the discriminative ability of features in the low-dimensional space. The experimental results on two real HSI data sets validate the effectiveness of our proposed MTLPP method.



Aircraft Type Recognition Based on Segmentation With Deep Convolutional Neural Networks

Feb. 2018

Aircraft type recognition in remote sensing images is a meaningful task. It remains challenging due to the difficulty of obtaining appropriate representation of aircrafts for recognition. To solve this problem, we propose a novel aircraft type recognition framework based on deep convolutional neural networks. First, an aircraft segmentation network is designed to obtain refined aircraft segmentation results which provide significant details to distinguish different aircrafts. Then, a keypoints’ detection network is proposed to acquire aircrafts’ directions and bounding boxes, which are used to align the segmentation results. A new multirotation refinement method is carefully designed to further improve the keypoints’ precision. At last, we apply a template matching method to identify aircrafts, and the intersection over union is adopted to evaluate the similarity between segmentation results and templates. The proposed framework takes advantage of both shape and scale information of aircrafts for recognition. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 95.6% accuracy on the challenging data set.



Aerial Scene Classification via Multilevel Fusion Based on Deep Convolutional Neural Networks

Feb. 2018

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature extractor and classifier can improve classification accuracy. In this letter, we construct three different convolutional neural networks with different sizes of receptive field, respectively. More importantly, we further propose a multilevel fusion method, which can make judgment by incorporating different levels’ information. The aerial image and two patches extracted from the image are fed to these three different networks, and then, a probability fusion model is established for final classification. The effectiveness of the proposed method is tested on a more challenging data set-AID that has 10000 high-resolution remote sensing images with 30 categories. Experimental results show that our multilevel fusion model gets a significant classification accuracy improvement over all state-of-the-art references.



Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network

Feb. 2018

With success of convolutional neural networks (CNNs) in computer vision, the CNN has attracted great attention in hyperspectral classification. Many deep learning-based algorithms have been focused on deep feature extraction for classification improvement. In this letter, a novel deep learning framework for hyperspectral classification based on a fully CNN is proposed. Through convolution, deconvolution, and pooling layers, the deep features of hyperspectral data are enhanced. After feature enhancement, the optimized extreme learning machine (ELM) is utilized for classification. The proposed framework outperforms the existing CNN and other traditional classification algorithms by including deconvolution layers and an optimized ELM. Experimental results demonstrate that it can achieve outstanding hyperspectral classification performance.



Temporal Change Detection in SAR Images Using Log Cumulants and Stacked Autoencoder

Feb. 2018

This letter proposes a change detection algorithm for damage assessment caused by fires in Ireland using Sentinel 1 data. The novelty, in this letter, is a feature extraction within tunable $Q$ discrete wavelet transform (TQWT) using higher order log cumulants of fractional Fourier transform (FrFT), which were fed into a stacked autoencoder (SAE) to distinguish changed and unchanged areas. The extracted features were used to train the SAE layerwise using an unsupervised learning algorithm. After training the decoding layer was replaced by a logistic regression layer to perform supervised fine-tuning and classification. The proposed algorithm was compared with the algorithm that used log cumulants of FrFT within the oriented dual-tree wavelet transform using support vector machine (SVM) classifier. The experimental results showed that the proposed combination of algorithms decreased the overall error (OE) for real synthetic aperture radar images by 6%, when TQWT was used instead of oriented dual-tree wavelet transform and OE was decreased by another 5% when SAE was used instead of the SVM classifier.



Detection of Vessel Targets in Sea Clutter Using In Situ Sea State Measurements With HFSWR

Feb. 2018

The detection of vessel targets could be effectively resolved in a high-frequency surface wave radar (HFSWR). However, signals reflected from vessels are concealed by sea clutter in the Doppler spectrum, where such detections are performed. Consequently, differences between these features in the Doppler domain cannot be readily observed, which greatly increases the difficulty in detecting vessel targets. In this letter, in situ sea state information is utilized to facilitate the detection of targets within sea clutter. First, the sea clutter spectrum, which is absent of vessel, is constructed. Second, sensitive sea clutter features that are influenced by vessel targets are selected and analyzed. Third, anomalies in sensitive sea clutter features are detected by obtaining respective thresholds. Finally, vessel targets are identified by the synthesized anomaly detection. Experimental results demonstrate the effectiveness of the proposed method, and the vessels detected using the HFSWR are further verified using synchronous automatic identification system information.



Corrections to “Problematic Projection to the In-Sample Subspace for a Kernelized Anomaly Detector” [Apr 16 485-489]

Feb. 2018

In the above paper [1], there are several errors, which we correct here.



Introducing IEEE Collabratec

Feb. 2018

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IEEE Geoscience and Remote Sensing Letters information for authors

Feb. 2018

Provides instructions and guidelines to prospective authors who wish to submit manuscripts.