# IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Includes the top 50 most frequently accessed documents for this publication according to the usage statistics for the month of

• ### Deep Learning-Based Classification of Hyperspectral Data

Publication Year: 2014, Page(s):2094 - 2107
Cited by:  Papers (146)
| |PDF (2730 KB) | HTML

Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we ... View full abstract»

• ### Object-Based Convolutional Neural Network for High-Resolution Imagery Classification

Publication Year: 2017, Page(s):3386 - 3396
| |PDF (1823 KB) | HTML

Timely and accurate classification and interpretation of high-resolution images are very important for urban planning and disaster rescue. However, as spatial resolution gets finer, it is increasingly difficultto recognize complex patterns in high-resolution remote sensing images. Deep learning offers an efficient strategy to fill the gap between complex image patterns and their semantic labels. H... View full abstract»

• ### Development of an Adaptive Approach for Precision Agriculture Monitoring with Drone and Satellite Data

Publication Year: 2017, Page(s):5322 - 5328
| |PDF (720 KB) | HTML

For better agricultural productivity and food management, there is an urgent need for precision agriculture monitoring at larger scales. In recent years, drones have been employed for precision agriculture monitoring at smaller scales, and for past few decades, satellite data are being used for land cover classification and agriculture monitoring at larger scales. The monitoring of agriculture pre... View full abstract»

• ### Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning

Publication Year: 2017, Page(s):3631 - 3640
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Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is desig... View full abstract»

• ### Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

Publication Year: 2014, Page(s):2405 - 2418
Cited by:  Papers (69)
| |PDF (4148 KB) | HTML

The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were... View full abstract»

• ### Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Publication Year: 2012, Page(s):354 - 379
Cited by:  Papers (747)
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Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates count... View full abstract»

• ### Surface Water Mapping by Deep Learning

Publication Year: 2017, Page(s):4909 - 4918
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Mapping of surface water is useful in a variety of remote sensing applications, such as estimating the availability of water, measuring its change in time, and predicting droughts and floods. Using the imagery acquired by currently active Landsat missions, a surface water map can be generated from any selected region as often as every 8 days. Traditional Landsat water indices require carefully sel... View full abstract»

• ### A Deep-Learning-Based Forecasting Ensemble to Predict Missing Data for Remote Sensing Analysis

Publication Year: 2017, Page(s):5228 - 5236
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The problem of missing data in remote sensing analysis is manifold. The situation becomes more serious during multitemporal analysis when data at various a-periodic timestamps are missing. In this work, we have proposed a deep-learning-based framework (Deep-STEP_FE) for reconstructing the missing data to facilitate analysis with remote sensing time series. The idea is to utilize the available data... View full abstract»

• ### Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network

Publication Year: 2015, Page(s):2381 - 2392
Cited by:  Papers (75)
| |PDF (2471 KB) | HTML

Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are pro... View full abstract»

• ### FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images

Publication Year: 2017, Page(s):5769 - 5783
| |PDF (1608 KB) | HTML

Sea-land segmentation and ship detection are two prevalent research domains for optical remote sensing harbor images and can find many applications in harbor supervision and management. As the spatial resolution of imaging technology improves, traditional methods struggle to perform well due to the complicated appearance and background distributions. In this paper, we unify the above two tasks int... View full abstract»

• ### GEROS-ISS: GNSS REflectometry, Radio Occultation, and Scatterometry Onboard the International Space Station

Publication Year: 2016, Page(s):4552 - 4581
Cited by:  Papers (16)
| |PDF (17798 KB) | HTML

GEROS-ISS stands for GNSS REflectometry, radio occultation, and scatterometry onboard the International Space Station (ISS). It is a scientific experiment, successfully proposed to the European Space Agency in 2011. The experiment as the name indicates will be conducted on the ISS. The main focus of GEROS-ISS is the dedicated use of signals from the currently available Global Navigation Satellite ... View full abstract»

• ### Two-Dimensional CS Adaptive FIR Wiener Filtering Algorithm for the Denoising of Satellite Images

Publication Year: 2017, Page(s):5245 - 5257
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In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adaptive finite-impulse response (FIR) filters driven by an optimization algorithm to self-adjust the filter coefficients, with applications in different domains of research. For signal processing applications, FIR Wiener filters are commonly used for noisy signal restorations by computing the statistical ... View full abstract»

• ### Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades

Publication Year: 2014, Page(s):70 - 91
Cited by:  Papers (41)
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This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hyperspectral imaging systems ranging from ground to airborne and satellite platforms have been designed, built, and operated. These include the field imaging spectrometer sy... View full abstract»

• ### Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network

Publication Year: 2017, Page(s):3011 - 3024
Cited by:  Papers (3)
| |PDF (1773 KB) | HTML

This paper proposes a novel framework for the fusion of hyperspectral and light detection and ranging-derived rasterized data using extinction profiles (EPs) and deep learning. In order to extract spatial and elevation information from both the sources, EPs that include different attributes (e.g., height, area, volume, diagonal of the bounding box, and standard deviation) are taken into account. T... View full abstract»

• ### Active Deep Learning for Classification of Hyperspectral Images

Publication Year: 2017, Page(s):712 - 724
Cited by:  Papers (3)
| |PDF (1248 KB) | HTML

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionar... View full abstract»

• ### Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest

Publication Year: 2016, Page(s):5560 - 5575
Cited by:  Papers (4)
| |PDF (1472 KB) | HTML

In this paper, we report the outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society. As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and mu... View full abstract»

• ### Extraction of Glacial Lake Outlines in Tibet Plateau Using Landsat 8 Imagery and Google Earth Engine

Publication Year: 2017, Page(s):4002 - 4009
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Glacial lake outburst floods (GLOFs) are among the most serious natural hazards in high mountain regions in the last several decades. The recent global warming has caused dramatic glacial lake changes and increased potential GLOF risk, particularly in Tibet Plateau (TP). Thus there is a pressing need to understand area and spatial distribution of glacial lakes at a large scale. Current efforts abo... View full abstract»

• ### Hyperspectral Image Superresolution by Transfer Learning

Publication Year: 2017, Page(s):1963 - 1974
Cited by:  Papers (5)
| |PDF (1027 KB) | HTML

Hyperspectral image superresolution is a highly attractive topic in computer vision and has attracted many researchers' attention. However, nearly all the existing methods assume that multiple observations of the same scene are required with the observed low-resolution hyperspectral image. This limits the application of superresolution. In this paper, we propose a new framework to enhance the reso... View full abstract»

• ### Initial Evaluation of SAR Capabilities in UAV Multicopter Platforms

Publication Year: 2018, Page(s):127 - 140
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Airborne synthetic aperture radar (SAR) sensors have been commonly used during the last decades to monitor different phenomena in medium-scale areas of observation, such as object detection and characterization or topographic mapping. The use of unmanned aerial vehicles (UAVs) is a cost-effective solution that offers higher operational flexibility than airborne systems to monitor these types of sc... View full abstract»

• ### Radar Remote Sensing of Agricultural Canopies: A Review

Publication Year: 2017, Page(s):2249 - 2273
Cited by:  Papers (4)
| |PDF (1005 KB) | HTML

Observations from spaceborne radar contain considerable information about vegetation dynamics. The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data. The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter fr... View full abstract»

• ### Image Classification Using RapidEye Data: Integration of Spectral and Textual Features in a Random Forest Classifier

Publication Year: 2017, Page(s):5334 - 5349
| |PDF (1416 KB) | HTML

Information on crop types derived from remotely sensed images provides valuable input for many applications such as crop growth modeling and yield forecasting. In this paper, a random forest (RF) classifier was used for crop classification using multispectral RapidEye imagery over two study sites, one in north-eastern China and one in eastern Ontario, Canada. Both vegetation indices (VIs) and text... View full abstract»

• ### R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method

Publication Year: 2017, Page(s):1975 - 1986
Cited by:  Papers (7)
| |PDF (1259 KB) | HTML

Deep-learning-based methods have displayed promising performance for hyperspectral image (HSI) classification, due to their capacity of extracting deep features from HSI. However, these methods usually require a large number of training samples. It is quite difficult for deep-learning model to provide representative feature expression for HSI data when the number of samples are limited. In this pa... View full abstract»

• ### A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results

Publication Year: 2013, Page(s):2102 - 2131
Cited by:  Papers (74)  |  Patents (3)
| |PDF (5115 KB) | HTML

A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 bill... View full abstract»

• ### OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation

Publication Year: 2018, Page(s):195 - 208
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With the rapid growth of Sentinel-1 synthetic aperture radar (SAR) data, how to exploit Sentinel-1 imagery and achieve effective and robust marine surveillance are crucial problems. In this paper, we present the OpenSARShip, a dataset dedicated to Sentinel-1 ship interpretation. The OpenSARShip, providing 11 346 SAR ship chips integrated with automatic identification system messages, owes five ess... View full abstract»

• ### Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks

Publication Year: 2017, Page(s):3652 - 3664
Cited by:  Papers (1)
| |PDF (1314 KB) | HTML

Vehicle detection in aerial images, being an interesting but challenging problem, plays an important role for a wide range of applications. Traditional methods are based on sliding-window search and handcrafted or shallow-learning-based features with heavy computational costs and limited representation power. Recently, deep learning algorithms, especially region-based convolutional neural networks... View full abstract»

• ### LiDAR Point Clouds to 3-D Urban Models$:$ A Review

Publication Year: 2018, Page(s):606 - 627
| |PDF (1201 KB) | HTML

Three-dimensional (3-D) urban models are an integral part of numerous applications, such as urban planning and performance simulation, mapping and visualization, emergency response training and entertainment, among others. We consolidate various algorithms proposed for reconstructing 3-D models of urban objects from point clouds. Urban models addressed in this review include buildings, vegetation,... View full abstract»

• ### A Novel Technique Based on Deep Learning and a Synthetic Target Database for Classification of Urban Areas in PolSAR Data

Publication Year: 2018, Page(s):154 - 170
| |PDF (2317 KB) | HTML Media

The classification of urban areas in polarimetric synthetic aperture radar (PolSAR) data is a challenging task. Moreover, urban structures oriented away from the radar line of sight pose an additional complexity in the classification process. The characterization of such areas is important for disaster relief and urban sprawl monitoring applications. In this paper, a novel technique based on deep ... View full abstract»

• ### Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

Publication Year: 2017, Page(s):5423 - 5441
| |PDF (6815 KB) | HTML

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide fa... View full abstract»

• ### Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

Publication Year: 2017, Page(s):3435 - 3447
| |PDF (1636 KB) | HTML

In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 image... View full abstract»

• ### Domain Adaptation Using Representation Learning for the Classification of Remote Sensing Images

Publication Year: 2017, Page(s):4198 - 4209
| |PDF (634 KB) | HTML

Traditional machine learning (ML) techniques are often employed to perform complex pattern recognition tasks for remote sensing images, such as land-use classification. In order to obtain acceptable classification results, these techniques require there to be sufficient training data available for every particular image. Obtaining training samples is challenging, particularly for near real-time ap... View full abstract»

• ### A Spectral-Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification

Publication Year: 2017, Page(s):5213 - 5227
| |PDF (1702 KB) | HTML

Hyperspectral image classification with limited labeled samples is a challenging task and still an open research issue. In this article, a novel technique is presented to address such an issue by exploiting dimensionality reduction, spectral-spatial information, and classification with active learning. The proposed technique is based on two phases. Considering the importance of dimensionality redu... View full abstract»

• ### A Fast Algorithm for SAR Image Segmentation Based on Key Pixels

Publication Year: 2017, Page(s):5657 - 5673
| |PDF (2406 KB) | HTML

Recent high-performance clustering methods process all pixels when segmenting an image, which results in a very large time complexity of these algorithms. Additionally, the performance of such algorithms can be severely affected by noise when dealing with highly polluted images. To address these problems, we propose a new unsupervised algorithm for segmenting synthetic aperture radar images based ... View full abstract»

• ### A Realistic FDTD Numerical Modeling Framework of Ground Penetrating Radar for Landmine Detection

Publication Year: 2016, Page(s):37 - 51
Cited by:  Papers (12)
| |PDF (2536 KB) | HTML

A three-dimensional (3-D) finite-difference time-domain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical ... View full abstract»

• ### A Novel Ozone Profile Shape Retrieval Using Full-Physics Inverse Learning Machine (FP-ILM)

Publication Year: 2017, Page(s):5442 - 5457
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Identifying ozone profile shapes from nadir-viewing satellite sensors is a critical yet challenging task for accurate reconstruction of vertical distributions of ozone relevant to climate change and air quality. Motivated by the need to develop a methodology to fast, reliably, and efficiently exploit ozone distributions and inspired by the success of machine learning, this paper introduces a novel... View full abstract»

• ### Iterative Reweighting Heterogeneous Transfer Learning Framework for Supervised Remote Sensing Image Classification

Publication Year: 2017, Page(s):2022 - 2035
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Supervised classification methods have been widely used in the hyperspectral remote sensing image analysis. However, they require a large number of training samples to guarantee good performance, which costs a large amount of time and human labor, motivating researchers to reuse labeled samples from the mass of pre-existing related images. Transfer learning methods can adapt knowledge in the exist... View full abstract»

• ### Estimation of Land Surface Temperature From MODIS Data for the Atmosphere With Air Temperature Inversion Profile

Publication Year: 2017, Page(s):2976 - 2983
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Air temperature inversion (ATI), which usually occurs at the near surface boundary layer in the atmosphere, influences the thermal path atmospheric upwelling and downwelling radiances. Because it is difficult to determine the occurrence of temperature inversion from satellite data, the influence of ATI on the retrieval of land surface temperature (LST) was not considered in the development of LST ... View full abstract»

• ### Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture Radar Data: Implications and Importance For Time-Series Analysis

Publication Year: 2018, Page(s):1 - 10
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The Sentinel-1 GRD (ground range detected) Level-1 product generated by the Instrument Processing Facility of the European Space Agency has noise artifacts at the image borders, which are quite consistent at both left and right sides of the satellite’s cross track and at the start and end of the data take along track. The Sentinel-1 border noise troubles the creation of clean and consistenc... View full abstract»

• ### Comparison and Evaluation of Different MODIS Aerosol Optical Depth Products Over the Beijing-Tianjin-Hebei Region in China

Publication Year: 2017, Page(s):835 - 844
Cited by:  Papers (4)
| |PDF (5391 KB) | HTML

Many aerosol retrieval algorithms based on the remote sensing technology have been developed and applied to produce aerosol optical depth (AOD) products for different satellite sensors. The dark target (DT) and deep blue (DB) algorithms are two main MODIS aerosol retrieval algorithms that are suitable for dark or bright areas. The estimation of land surface reflectance (LSR) is necessary to improv... View full abstract»

• ### A Comparative Study of Operational Vessel Detectors for Maritime Surveillance Using Satellite-Borne Synthetic Aperture Radar

Publication Year: 2016, Page(s):2687 - 2701
Cited by:  Papers (7)
| |PDF (2368 KB) | HTML

This paper presents a comparative study among four operational detectors that work by automatically post-processing synthetic aperture radar (SAR) images acquired from the satellite platforms RADARSAT-2 and COSMO-SkyMed. Challenging maritime scenarios have been chosen to assess the detectors' performance against features such as ambiguities, significant sea clutter, or irregular shorelines. The SA... View full abstract»

• ### In-Memory Parallel Processing of Massive Remotely Sensed Data Using an Apache Spark on Hadoop YARN Model

Publication Year: 2017, Page(s):3 - 19
Cited by:  Papers (2)
| |PDF (3007 KB) | HTML

MapReduce has been widely used in Hadoop for parallel processing larger-scale data for the last decade. However, remote-sensing (RS) algorithms based on the programming model are trapped in dense disk I/O operations and unconstrained network communication, and thus inappropriate for timely processing and analyzing massive, heterogeneous RS data. In this paper, a novel in-memory computing framework... View full abstract»

• ### Learning Hierarchical Features for Automated Extraction of Road Markings From 3-D Mobile LiDAR Point Clouds

Publication Year: 2015, Page(s):709 - 726
Cited by:  Papers (19)
| |PDF (3803 KB) | HTML

This paper presents a novel method for automated extraction of road markings directly from three dimensional (3-D) point clouds acquired by a mobile light detection and ranging (LiDAR) system. First, road surface points are segmented from a raw point cloud using a curb-based approach. Then, road markings are directly extracted from road surface points through multisegment thresholding and spatial ... View full abstract»

• ### Spatial–Spectral-Graph-Regularized Low-Rank Tensor Decomposition for Multispectral and Hyperspectral Image Fusion

Publication Year: 2018, Page(s):1 - 11
| |PDF (758 KB)

Hyperspectral (HS) and multispectral (MS) image fusion aims at producing high-resolution HS (HRHS) images. However, the existing methods could not simultaneously consider the structures in both the spatial and spectral domains of the HS cube. In order to effectively preserve spatial–spectral structures in HRHS images, we propose a new low-resolution HS (LRHS) and high-resolution MS (HRMS) i... View full abstract»

• ### An Improved Superpixel-Level CFAR Detection Method for Ship Targets in High-Resolution SAR Images

Publication Year: 2018, Page(s):184 - 194
| |PDF (1041 KB) | HTML

To achieve efficient ship detection in high-resolution synthetic aperture radar images, an improved superpixel-level constant false alarm rate (CFAR) detection method is proposed with three modifications. First, the weighted information entropy (WIE) describes the statistical characteristics of superpixels, yielding a better distinction between target and clutter superpixels. Second, a two-stage C... View full abstract»

• ### Daily Evapotranspiration Mapping Using Regression Random Forest Models

Publication Year: 2017, Page(s):5359 - 5368
| |PDF (975 KB) | HTML

Efficient water management in agriculture requires an accurate estimation of evapotranspiration (ET). Even though local measurements can be used to estimate the components of the surface energy balance, these values cannot be extrapolated to large areas due to the heterogeneity and complexity of agricultural and natural land surfaces and the dynamic nature of their heat processes. This extrapolati... View full abstract»

• ### Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass

Publication Year: 2017, Page(s):5569 - 5582
| |PDF (1312 KB) | HTML

Timely, spatially complete, and reliable forest above-ground biomass (AGB) data are a prerequisite to support forest management and policy formulation. Traditionally, forest AGB is spatially estimated by integrating satellite images, in particular, optical data, with field plots from forest inventory programs. However, field data are limited in remote and unmanaged areas. In addition, optical refl... View full abstract»

• ### Scene Classification via Triplet Networks

Publication Year: 2018, Page(s):220 - 237
| |PDF (1295 KB) | HTML

Scene classification is a fundamental task for automatic remote sensing image understanding. In recent years, convolutional neural networks have become a hot research topic in the remote sensing community, and have made great achievements in scene classification. Deep convolutional networks are primarily trained in a supervised way, requiring huge volumes of labeled training samples. However, clea... View full abstract»

• ### Ground Moving Target Imaging Based on Keystone Transform and Coherently Integrated CPF With a Single-Channel SAR

Publication Year: 2017, Page(s):5686 - 5694
| |PDF (801 KB) | HTML

It is well known that ground moving targets may be smeared in a synthetic aperture radar (SAR) image due to the range migration, Doppler phase broadening, and velocity ambiguity caused by target unknown motion parameters, especially for the accelerating targets within a long observation time. To deal with these problems, a maneuvering target refocusing method based on keystone transform (KT) and c... View full abstract»

• ### Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems

Publication Year: 2009, Page(s):2 - 10
Cited by:  Papers (237)  |  Patents (3)
| |PDF (1056 KB) | HTML

The contribution of power production by photovoltaic (PV) systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production. This forecast information is necessary for the management of the electricity grids and for solar energy trading. This paper presents an approac... View full abstract»

• ### Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest

Publication Year: 2016, Page(s):5547 - 5559
Cited by:  Papers (6)
| |PDF (1680 KB) | HTML

In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed ... View full abstract»

• ### Sparsity and Low-Rank Dictionary Learning for Sparse Representation of Monogenic Signal

Publication Year: 2018, Page(s):141 - 153
| |PDF (1365 KB) | HTML

This paper proposes a new framework of dictionary learning for a recently developed study, sparse representation of monogenic signal. The proposed framework is applied to target recognition in SAR image. Unlike the preceding works, where the sparse model is formed via an overcomplete dictionary whose atoms are the training samples themselves, a new approach to learn a more discriminative dictionar... View full abstract»

## Aims & Scope

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS) addresses current issues and techniques in applied remote and in situ sensing, their integration, and applied modeling and information creation for understanding the Earth.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Dr. Qian (Jenny) Du
Mississippi State University