IEEE Geoscience and Remote Sensing Letters
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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.
Latest Published Articles
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A Method for Weak Target Detection Based on Human Visual Contrast Mechanism
Sun Dec 09 00:00:00 EST 2018 Sun Dec 09 00:00:00 EST 2018 -
A Liquid Crystal Tunable Filter-Based Hyperspectral LiDAR System and Its Application on Vegetation Red Edge Detection
Wed Dec 05 00:00:00 EST 2018 Wed Dec 05 00:00:00 EST 2018 -
A Modified Min-Norm for Time Delay and Interface Roughness Estimation by Ground Penetrating Radar: Experimental Results
Sun Oct 28 00:00:00 EDT 2018 Sun Oct 28 00:00:00 EDT 2018 -
Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images
Sun Oct 28 00:00:00 EDT 2018 Sun Oct 28 00:00:00 EDT 2018 -
Multiscale Sparse Features Embedded 4-Points Congruent Sets for Global Registration of TLS Point Clouds
Fri Oct 26 00:00:00 EDT 2018 Fri Oct 26 00:00:00 EDT 2018
Popular Articles
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Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
Fri Mar 31 00:00:00 EDT 2017 Fri Mar 31 00:00:00 EDT 2017 -
Convolutional Neural Network With Data Augmentation for SAR Target Recognition
Tue Jan 26 00:00:00 EST 2016 Tue Jan 26 00:00:00 EST 2016 -
Deep Learning Based Feature Selection for Remote Sensing Scene Classification
Fri Sep 18 00:00:00 EDT 2015 Fri Sep 18 00:00:00 EDT 2015 -
Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks
Tue Mar 25 00:00:00 EDT 2014 Tue Mar 25 00:00:00 EDT 2014 -
Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks
Fri Dec 29 00:00:00 EST 2017 Fri Dec 29 00:00:00 EST 2017
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Meet Our Editors
Editor-in-Chief
AVIK BHATTACHARYA
Centre of Studies in Resources Engineering (CSRE)
Indian Institute of Technology Bombay
Mumbai, Maharashtra 400076, India
Popular Documents (January 2019)
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Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
Publication Year: 2017, Page(s):778 - 782
Cited by: Papers (81)Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing ... View full abstract»
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Convolutional Neural Network With Data Augmentation for SAR Target Recognition
Publication Year: 2016, Page(s):364 - 368
Cited by: Papers (67)Many methods have been proposed to improve the performance of synthetic aperture radar (SAR) target recognition but seldom consider the issues in real-world recognition systems, such as the invariance under target translation, the invariance under speckle variation in different observations, and the tolerance of pose missing in training data. In this letter, we investigate the capability of a deep... View full abstract»
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Deep Learning Based Feature Selection for Remote Sensing Scene Classification
Publication Year: 2015, Page(s):2321 - 2325
Cited by: Papers (86)With the popular use of high-resolution satellite images, more and more research efforts have been placed on remote sensing scene classification/recognition. In scene classification, effective feature selection can significantly boost the final performance. In this letter, a novel deep-learning-based feature-selection method is proposed, which formulates the feature-selection problem as a feature ... View full abstract»
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Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks
Publication Year: 2014, Page(s):1797 - 1801
Cited by: Papers (182)Detecting small objects such as vehicles in satellite images is a difficult problem. Many features (such as histogram of oriented gradient, local binary pattern, scale-invariant feature transform, etc.) have been used to improve the performance of object detection, but mostly in simple environments such as those on roads. Kembhavi et al. proposed that no satisfactory accuracy has been achieved in ... View full abstract»
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Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks
Publication Year: 2018, Page(s):212 - 216
Cited by: Papers (3)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 adve... View full abstract»
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Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks
Publication Year: 2016, Page(s):8 - 12
Cited by: Papers (83)We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in the conventional supervised learning paradigm that relies on the design of handcrafted features. Whereas these schemes attained high accuracy, the requirement for domain knowledge of each problem limits ... View full abstract»
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Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks
Publication Year: 2016, Page(s):1935 - 1939
Cited by: Papers (53)Deep convolutional neural networks have achieved great success in computer vision and many other areas. They automatically extract translational-invariant spatial features and integrate with neural network-based classifier. This letter investigates the suitability and potential of deep convolutional neural network in supervised classification of polarimetric synthetic aperture radar (POLSAR) image... View full abstract»
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A Framework for Remote Sensing Images Processing Using Deep Learning Techniques
Publication Year: 2019, Page(s):25 - 29Deep learning (DL) techniques are becoming increasingly important to solve a number of image processing tasks. Among common algorithms, convolutional neural network- and recurrent neural network-based systems achieve state-of-the-art results on satellite and aerial imagery in many applications. While these approaches are subject to scientific interest, there is currently no operational and generic... View full abstract»
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Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery
Publication Year: 2017, Page(s):549 - 553
Cited by: Papers (43)Deep convolutional neural networks (DCNNs) have recently emerged as a dominant paradigm for machine learning in a variety of domains. However, acquiring a suitably large data set for training DCNN is often a significant challenge. This is a major issue in the remote sensing domain, where we have extremely large collections of satellite and aerial imagery, but lack the rich label information that i... View full abstract»
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Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization
Publication Year: 2015, Page(s):309 - 313
Cited by: Papers (105)A new feature selection approach that is based on the integration of a genetic algorithm and particle swarm optimization is proposed. The overall accuracy of a support vector machine classifier on validation samples is used as a fitness value. The new approach is carried out on the well-known Indian Pines hyperspectral data set. Results confirm that the new approach is able to automatically select... View full abstract»
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Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks
Publication Year: 2016, Page(s):105 - 109
Cited by: Papers (91)Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entire... View full abstract»
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Identifying Corresponding Patches in SAR and Optical Images With a Pseudo-Siamese CNN
Publication Year: 2018, Page(s):784 - 788
Cited by: Papers (4)In this letter, we propose a pseudo-siamese convolutional neural network architecture that enables to solve the task of identifying corresponding patches in very high-resolution optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a... View full abstract»
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Deformable Convolutional Neural Networks for Hyperspectral Image Classification
Publication Year: 2018, Page(s):1254 - 1258
Cited by: Papers (4)Convolutional neural networks (CNNs) have recently been demonstrated to be a powerful tool for hyperspectral image (HSI) classification, since they adopt deep convolutional layers whose kernels can effectively extract high-level spatial-spectral features. However, sampling locations of traditional convolutional kernels are fixed and cannot be changed according to complex spatial structures in HSIs... View full abstract»
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Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data
Publication Year: 2014, Page(s):1840 - 1843
Cited by: Papers (139)The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new ... View full abstract»
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PolSAR Image Classification Using Polarimetric-Feature-Driven Deep Convolutional Neural Network
Publication Year: 2018, Page(s):627 - 631
Cited by: Papers (5)Polarimetric synthetic aperture radar (PolSAR) image classification is an important application. Advanced deep learning techniques represented by deep convolutional neural network (CNN) have been utilized to enhance the classification performance. One current challenge is how to adapt deep CNN classifier for PolSAR classification with limited training samples, while keeping good generalization per... View full abstract»
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SAR Target Detection Based on SSD With Data Augmentation and Transfer Learning
Publication Year: 2019, Page(s):150 - 154In this letter, the single shot multibox detector (SSD), which is a real-time object detection method based on convolutional neural network, is applied to realize target detection for synthetic aperture radar (SAR) images. Since there are no sufficient labeled images for training in SAR target detection, we apply two strategies, data augmentation and transfer learning. For data augmentation, the f... View full abstract»
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A Conditional Adversarial Network for Change Detection in Heterogeneous Images
Publication Year: 2019, Page(s):45 - 49Due to the distinct statistical properties in cross-sensor images, change detection in heterogeneous images is much more challenging than in homogeneous images. In this letter, we adopt a conditional generative adversarial network (cGAN) to transform the heterogeneous synthetic aperture radar (SAR) and optical images into some space where their information has a more consistent representation, mak... View full abstract»
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High-Resolution SAR Image Classification via Deep Convolutional Autoencoders
Publication Year: 2015, Page(s):2351 - 2355
Cited by: Papers (54)Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature representation and the presence of speckle noise in SAR images make classification difficult to handle. In order to overcome these problems, a deep convolutional autoencoder (DCAE) is proposed to extract features and conduct classification automatically.... View full abstract»
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Instant Object Detection in Lidar Point Clouds
Publication Year: 2017, Page(s):992 - 996
Cited by: Papers (3)In this letter, we present a new approach for object classification in continuously streamed Lidar point clouds collected from urban areas. The input of our framework is raw 3-D point cloud sequences captured by a Velodyne HDL-64 Lidar, and we aim to extract all vehicles and pedestrians in the neighborhood of the moving sensor. We propose a complete pipeline developed especially for distinguishing... View full abstract»
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Convolutional Neural Network Based Automatic Object Detection on Aerial Images
Publication Year: 2016, Page(s):740 - 744
Cited by: Papers (39)We are witnessing daily acquisition of large amounts of aerial and satellite imagery. Analysis of such large quantities of data can be helpful for many practical applications. In this letter, we present an automatic content-based analysis of aerial imagery in order to detect and mark arbitrary objects or regions in high-resolution images. For that purpose, we proposed a method for automatic object... View full abstract»
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Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and
Publication Year: 2009, Page(s):772 - 776 -Means Clustering$k$
Cited by: Papers (216) | Patents (1)In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and k-means clustering. The difference image is partitioned into h times h nonoverlapping blocks. S, S les h2, orthonormal eigenvectors are extracted through PCA of h times h nonoverlapping block set to create an eigenvector space. Eac... View full abstract»
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Multiview Deep Learning for Land-Use Classification
Publication Year: 2015, Page(s):2448 - 2452
Cited by: Papers (70)A multiscale input strategy for multiview deep learning is proposed for supervised multispectral land-use classification, and it is validated on a well-known data set. The hypothesis that simultaneous multiscale views can improve composition-based inference of classes containing size-varying objects compared to single-scale multiview is investigated. The end-to-end learning system learns a hierarc... View full abstract»
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Objects Segmentation From High-Resolution Aerial Images Using U-Net With Pyramid Pooling Layers
Jun Hee Kim ; Haeyun Lee ; Seonghwan J. Hong ; Sewoong Kim ; Juhum Park ; Jae Youn Hwang ; Jihwan P. ChoiPublication Year: 2019, Page(s):115 - 119Extracting manufactured features such as buildings, roads, and water from aerial images is critical for urban planning, traffic management, and industrial development. Recently, convolutional neural networks (CNNs) have become a popular strategy to capture contextual features automatically. In order to train CNNs, a large training data are required, but it is not straightforward to use free-access... View full abstract»
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Vehicle Detection in High-Resolution Images Using Superpixel Segmentation and CNN Iteration Strategy
Publication Year: 2019, Page(s):105 - 109This letter presents a study of vehicle detection in high-resolution images using superpixel segmentation and iterative convolutional neural network strategy. First, a novel superpixel segmentation integrated with multiple local information constraints method is proposed to improve the segmentation results with a low breakage rate. To make training and detection more efficient, we extract meaningf... View full abstract»
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Road Extraction by Deep Residual U-Net
Publication Year: 2018, Page(s):749 - 753
Cited by: Papers (8)Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model are twofold: first... 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.
Meet Our Editors
Editor-in-Chief
AVIK BHATTACHARYA
Centre of Studies in Resources Engineering (CSRE)
Indian Institute of Technology Bombay
Mumbai, Maharashtra 400076, India
Further Links
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. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.
Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=8859 More »
Frequency: 12
ISSN: 1545-598X
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Subjects
- Geoscience
Contacts
Editor-in-Chief
AVIK BHATTACHARYA
Centre of Studies in Resources Engineering (CSRE)
Indian Institute of Technology Bombay
Mumbai, Maharashtra 400076, India
avik.bhattach@gmail.com
Editorial Board
Fauzia Ahmad
Timo Balz
Yakoub Bazi
Jon Atli Benediktsson
Subrahmanyam Bulusu
Gustavo Camps-Valls
Turgay Celik
Liang Cheng
Renato Cintra
Susanne Craig
Xiaoli Deng
Peijun Du
Yang Du
Giampaolo Ferraioli
Gianfranco Fornaro
Bo-Cai Gao
James Garrison
Luis Gomez-Deniz
Francisco Grings
Merrick C. Haller
Xin Huang
Frederic Jacob
Gary Jedlovec
Rui Jin
Jasmeet Judge
Hyung-Sup Jung
David Long
Xiaoqiang Lu
Tom Lukowski
Nelson Mascarenhas
Farid Melgani
Sidarth Misra
M. Montopoli
Gabriele Moser
Mario Parente
Javier Plaza
Luca Pulvirenti
Motoyuki Sato
Michael Schmitt
Jiangchen Shi
Michal Shimoni
C. K. Shum
Mark Sletten
Salvatore Stramondo
Qi Wang
Marie Weiss
Chenglu Wen
Feng Xu
Marwan Younis
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Contacts
Editor-in-Chief
AVIK BHATTACHARYA
Centre of Studies in Resources Engineering (CSRE)
Indian Institute of Technology Bombay
Mumbai, Maharashtra 400076, India