IEEE Geoscience and Remote Sensing Letters

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• Front Cover

Publication Year: 2019, Page(s): C1
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• IEEE Geoscience and Remote Sensing Letters publication information

Publication Year: 2019, Page(s): C2
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Publication Year: 2019, Page(s):161 - 162
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• Effect of Prompt Particle Events on OLCI Ocean Color Imagery in the South Atlantic Anomaly: Detection and Removal

Publication Year: 2019, Page(s):163 - 167
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It has been found that cosmic rays and massive charged particles trapped in the magnetosphere or arriving from the sun might produce spike noise over the dark offset signal coming from charge-coupled devices (CCDs) on optic sensors such as Ocean and Land Color Instrument (Sentinel-3/OLCI) and Medium Resolution Imaging Spectrometer (Envisat/MERIS). These phenomena are called prompt particle events ... View full abstract»

• On the Estimation of Wind Speed Diurnal Cycles Using Simulated Measurements of CYGNSS and ASCAT

Publication Year: 2019, Page(s):168 - 172
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Due to solar heating, many geophysical quantities, such as precipitation, sea surface temperature, and wind, have distinct diurnal signatures in the tropical region. To explore the potential of wind speed measurements of the Cyclone Global Navigation Satellite System (CYGNSS) mission launched in December 2016, we examine the degree to which wind diurnal cycles can be estimated from the surface win... View full abstract»

• Inpainting of Remote Sensing SST Images With Deep Convolutional Generative Adversarial Network

Publication Year: 2019, Page(s):173 - 177
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Cloud occlusion is a common problem in the satellite remote sensing (RS) field and poses great challenges for image processing and object detection. Most existing methods for cloud occlusion recovery extract the surrounding information from the single corrupted image rather than the historical RS image records. Moreover, the existing algorithms can only handle small and regular-shaped obnubilation... View full abstract»

• A Multiscale Deep Framework for Ocean Fronts Detection and Fine-Grained Location

Publication Year: 2019, Page(s):178 - 182
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Ocean front plays an important role in marine fishery production and biogeochemical cycling. This letter proposes a multiscale deep framework to meet the need for automatic ocean front detection and fine-grained location. The framework mainly focuses on bringing a well-trained deep learning model into front detection and location on the global satellite sea surface temperature image. First, a mult... View full abstract»

• Analysis of Spectral Bands and Spatial Resolutions for Weed Classification Via Deep Convolutional Neural Network

Publication Year: 2019, Page(s):183 - 187
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Weed detection and classification are one of the important and crucial steps for area-specific weed control. This reduces the overall cost and the negative impact of using unnecessary herbicides on human health and crops. As the spectral similarity between weeds and crops is high, patch-based classification approaches are adopted in this letter. Convolutional neural network (CNN) and histogram of ... View full abstract»

• A Modified Min-Norm for Time Delay and Interface Roughness Estimation by Ground Penetrating Radar: Experimental Results

Publication Year: 2019, Page(s):188 - 191
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The development of methods and tools for the road infrastructure sustainable management is a research challenge, especially for nondestructive testing methods. This letter focuses on the estimation of the thickness of civil engineering structures, like pavements, and more precisely, the time delay and interface roughness. We propose a modified Min-Norm algorithm which allows efficiently estimating... View full abstract»

• Long Short-Term Memory and Variational Autoencoder With Convolutional Neural Networks for Generating NMR T2 Distributions

Publication Year: 2019, Page(s):192 - 195
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Downhole nuclear magnetic resonance (NMR) logs acquired in the borehole environment are valuable for subsurface characterization because they contain information about the pore size distribution, fluid composition, fluid saturation, fluid mobility, formation permeability, and porosity. NMR log acquisition can be challenging due to operational and financial constraints. Recently, NMR T2 distributio... View full abstract»

• Multisensor Composite Kernels Based on Extreme Learning Machines

Publication Year: 2019, Page(s):196 - 200
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In this letter, we first propose multisensor composite kernel (MCK) extreme learning machines to fuse hyperspectral and light detection and ranging (LiDAR) features effectively. Then, based on the MCK, we develop a fully automatic fusion framework. In the proposed framework, spatial and elevation features of hyperspectral and LiDAR data are first extracted using extinction profiles. Then, hyperspe... View full abstract»

• An Adaptive Multifeature Method for Semiautomatic Road Extraction From High-Resolution Stereo Mapping Satellite Images

Publication Year: 2019, Page(s):201 - 205
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In this letter, an adaptive multifeature method for semiautomatic road extraction in high-resolution stereo mapping satellite images is proposed. First, the digital surface model (DSM) is generated from high-resolution stereo mapping satellite images by the semiglobal vertical line locus matching method. To combine the entropy feature and the spectral feature with the DSM, an adaptive method based... View full abstract»

• A Coherent Integration Method for Moving Target Detection Using Frequency Agile Radar

Publication Year: 2019, Page(s):206 - 210
Cited by:  Papers (2)
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This letter addresses the coherent integration problem for the moving target detection in a frequency agile radar system. Due to the random phase fluctuation caused by the carrier frequency random hopping, the complex range–azimuth coupling effects will significantly deteriorate the target integration performance. In this letter, echoes are classified into different bursts according to the carrier... View full abstract»

• Observation and Study of the Aspect Sensitivity and Echo Mechanism Based on the Wuhan MST Radar

Publication Year: 2019, Page(s):211 - 215
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The observations of the Wuhan mesosphere, stratosphere, and troposphere radar with different tilted angles and observational modes are used to study the aspect sensitivity of the radar echoes and discuss the echo mechanism in the lower thermosphere, mesosphere, lower stratosphere, and troposphere. This letter indicates that: 1) the variation of radar echoes with different tilted angles is more obv... View full abstract»

• Low Complexity Algorithm for Range-Point Migration-Based Human Body Imaging for Multistatic UWB Radars

Publication Year: 2019, Page(s):216 - 220
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High-resolution, short-range sensors that can be applied in optically challenging environments (e.g., in the presence of clouds, fog, and/or dark smog) are in high demand for various applications. Ultrawideband radar is a promising sensor that is suitable for short-range surveillance or watching sensors. Range-point migration (RPM) has been recently established as a promising imaging approach to a... View full abstract»

• Robust Radial Velocity Estimation Based on Joint-Pixel Normalized Sample Covariance Matrix and Shift Vector for Moving Targets

Publication Year: 2019, Page(s):221 - 225
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The clutter suppression and target radial velocity estimation are essential in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. In reality, the heterogeneous clutter, the image coregistration error, and channel mismatch will remarkably decline the estimation performance of the target radial velocity. To address these issues, a robust radial v... View full abstract»

• A Novel Helicopter-Borne RoSAR Imaging Algorithm Based on the Azimuth Chirp $z$ transform

Publication Year: 2019, Page(s):226 - 230
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A rotating synthetic aperture radar (RoSAR) can image the environment in 360° on a stationary platform because it uses rotating antennas. The range-variant distortion in azimuth is evident in the RoSAR system, which degrades the imaging quality, particularly in high-resolution situations. In this letter, a new chirp $z$ trans... View full abstract»

• An Iterative Feedback-Based Change Detection Algorithm for Flood Mapping in SAR Images

Publication Year: 2019, Page(s):231 - 235
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This letter proposes a novel algorithm for the unsupervised detection of flood mapping in synthetic aperture radar (SAR) images. In the literature, unsupervised change detection of SAR images mainly consists of two steps, i.e., first generating a difference image from two given images and then binarizing the difference image to produce the desired change map. Conventional change detection algorith... View full abstract»

• 3-D Inverse Synthetic Aperture Ladar Imaging and Scaling of Space Debris Based on the Fractional Fourier Transform

Publication Year: 2019, Page(s):236 - 240
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The inverse synthetic aperture ladar (ISAL) is an important method for observation and imaging of space targets. Here, a 3-D ISAL imaging algorithm is proposed for spinning targets such as space debris. Since laser wavelength is 4–5 orders of magnitude smaller than that of microwave, the Doppler frequency caused by target motion is more pronounced in ISAL. Doppler frequency modulation rates can be... View full abstract»

• Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification

Publication Year: 2019, Page(s):241 - 245
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Collecting labeled samples is quite costly and time-consuming for hyperspectral image (HSI) classification task. Semisupervised learning framework, which combines the intrinsic information of labeled and unlabeled samples, can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this letter, we propose a novel semisupervised learning framework that is based o... View full abstract»

• Sparse Representation-Based Hyperspectral Image Classification Using Multiscale Superpixels and Guided Filter

Publication Year: 2019, Page(s):246 - 250
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We propose a spatial-spectral hyperspectral image classification method based on multiscale superpixels and guided filter (MSS-GF). In order to use spatial information effectively, MSSs are used to get local information from different region scales. Sparse representation classifier is used to generate classification maps for each region scale. Then, multiple binary probability maps are obtained fo... View full abstract»

• DeepLab-Based Spatial Feature Extraction for Hyperspectral Image Classification

Publication Year: 2019, Page(s):251 - 255
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Recently, deep learning has been used for hyperspectral image classification (HSIC) due to its powerful feature learning and classification ability. In this letter, a novel deep learning-based framework based on DeepLab is proposed for HSIC. Inspired by the excellent performance of DeepLab in semantic segmentation, the proposed framework applies DeepLab to excavate spatial features of the hyperspe... View full abstract»

• A Coarse-to-Fine Method for Infrared Small Target Detection

Publication Year: 2019, Page(s):256 - 260
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Infrared small target detection in a complex background is a challenging problem. A complex background generally contains structured edges, unstructured clutter, and noise, which completely have different properties. It is very difficult to separate small target from these interferences by exploiting one property. To solve this problem, we propose a coarse-to-fine method to gradually detect small ... View full abstract»

• A Method for Weak Target Detection Based on Human Visual Contrast Mechanism

Publication Year: 2019, Page(s):261 - 265
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In order to detect space weak targets in low signal-to-clutter ratio (SCR) environment, this letter presents an effective detection model. At the first stage, two-dimensional least-mean-square preprocessing part is applied to the original image, after which, the suspicious area can be obtained. At the second stage, a novel method based on the contrast mechanism of human visual system called neighb... View full abstract»

• Triplet-Based Semantic Relation Learning for Aerial Remote Sensing Image Change Detection

Publication Year: 2019, Page(s):266 - 270
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This letter presents a novel supervised change detection method based on a deep siamese semantic network framework, which is trained by using improved triplet loss function for optical aerial images. The proposed framework can not only extract features directly from image pairs which include multiscale information and are more abstract as well as robust, but also enhance the interclass separabilit... View full abstract»

Aims & Scope

IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
AVIK BHATTACHARYA
Centre of Studies in Resources Engineering (CSRE)
Indian Institute of Technology Bombay
Mumbai, Maharashtra 400076, India