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

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

Publication Year: 2014, Page(s):2094 - 2107
Cited by:  Papers (146)
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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»

• ### Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest

Publication Year: 2018, Page(s):1363 - 1377
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In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data ... View full abstract»

• ### Remote Sensing Image Fusion With Deep Convolutional Neural Network

Publication Year: 2018, Page(s):1656 - 1669
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Remote sensing images with different spatial and spectral resolution, such as panchromatic (PAN) images and multispectral (MS) images, can be captured by many earth-observing satellites. Normally, PAN images possess high spatial resolution but low spectral resolution, while MS images have high spectral resolution with low spatial resolution. In order to integrate spatial and spectral information c... View full abstract»

• ### Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks

Publication Year: 2018, Page(s):821 - 829
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We propose a novel spatiotemporal fusion method based on deep convolutional neural networks (CNNs) under the application background of massive remote sensing data. In the training stage, we build two five-layer CNNs to deal with the problems of complicated correspondence and large spatial resolution gaps between MODIS and Landsat images. Specifically, we first learn a nonlinear mapping CNN between... 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»

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

Publication Year: 2015, Page(s):2381 - 2392
Cited by:  Papers (75)
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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»

• ### Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images

Publication Year: 2018, Page(s):1633 - 1644
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Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks ... 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)
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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»

• ### 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)
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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»

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

Publication Year: 2017, Page(s):3386 - 3396
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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»

• ### 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»

• ### Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations

Publication Year: 2018, Page(s):730 - 742
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This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn ful... 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)
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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»

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

Publication Year: 2014, Page(s):2405 - 2418
Cited by:  Papers (69)
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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»

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

Publication Year: 2018, Page(s):606 - 627
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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»

• ### Dehazing for Multispectral Remote Sensing Images Based on a Convolutional Neural Network With the Residual Architecture

Publication Year: 2018, Page(s):1645 - 1655
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Multispectral remote sensing images are often contaminated by haze, which causes low image quality. In this paper, a novel dehazing method based on a deep convolutional neural network (CNN) with the residual structure is proposed for multispectral remote sensing images. First, multiple CNN individuals with the residual structure are connected in parallel and each individual is used to learn a regr... 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)
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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»

• ### 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»

• ### Robust Traffic-Sign Detection and Classification Using Mobile LiDAR Data With Digital Images

Publication Year: 2018, Page(s):1715 - 1724
| | PDF (888 KB) | HTML

This study aims at building a robust method for detecting and classifying traffic signs from mobile LiDAR point clouds and digital images. First, this method detects traffic signs from mobile LiDAR point clouds with regard to a prior knowledge of road width, pole height, reflectance, geometrical structure, and traffic-sign size. Then, traffic-sign images are segmented by projecting the detected tr... 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»

• ### 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»

• ### 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»

• ### Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform

Publication Year: 2018, Page(s):1694 - 1714
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Mobile light detection and ranging (LiDAR) systems are widely used to generate precise 3-D spatial information, which in turn aids a variety of applications such as digital building model generation, transportation corridor asset management, telecommunications, precision agriculture, and infrastructure monitoring. Integrating such systems with one or more cameras would allow forward and backward p... View full abstract»

• ### Classification of Hyperspectral Images by Gabor Filtering Based Deep Network

Publication Year: 2018, Page(s):1166 - 1178
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In this paper, a novel spectral-spatial classification method based on Gabor filtering and deep network (GFDN) is proposed. First, Gabor features are extracted by performing Gabor filtering on the first three principal components of the hyperspectral image, which can typically characterize the low-level spatial structures of different orientations and scales. Then, the Gabor features and spectral ... View full abstract»

• ### Semiautomated Segmentation of Sentinel-1 SAR Imagery for Mapping Sea Ice in Labrador Coast

Publication Year: 2018, Page(s):1419 - 1432
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This study aims at proposing a semiautomated sea ice segmentation workflow utilizing Sentinel-1 synthetic aperture radar imagery. The workflow consists of two main steps. First, preferable features in sea ice interpretation were determined with a random forest feature selection method. Second, an unsupervised graph-cut image segmentation was performed. The workflow was tested on 13 Sentinel-1A ima... View full abstract»

• ### Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition

Publication Year: 2018, Page(s):1227 - 1243
| | PDF (2500 KB) | HTML

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the i... View full abstract»

• ### Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation

Publication Year: 2018, Page(s):1520 - 1532
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Landslide inventory mapping (LIM) plays an important role in hazard assessment and hazard relief. Even though much research has taken place in past decades, there is space for improvements in accuracy and the usability of mapping systems. In this paper, a new landslide inventory mapping framework is proposed based on the integration of the majority voting method and the multiscale segmentation of ... View full abstract»

• ### A Robust Fuzzy C-Means Algorithm Based on Bayesian Nonlocal Spatial Information for SAR Image Segmentation

Publication Year: 2018, Page(s):896 - 906
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The fuzzy c-means (FCM) algorithm and many improved algorithms incorporating spatial information have been proven to be effective in image segmentation. However, these methods are not adaptable to process synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. Our solution, which enables the effective segmentation of SAR images by guaranteeing noise-immunity and edge detail 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»

• ### Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

Publication Year: 2018, Page(s):962 - 977
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Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and ... View full abstract»

• ### SAR Image Land Cover Datasets for Classification Benchmarking of Temporal Changes

Publication Year: 2018, Page(s):1571 - 1592
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The increased availability of high-resolution synthetic aperture radar (SAR) satellite images has led to new civil applications of these data. Among them is the systematic classification of land cover types based on the patterns of settlements or agriculture recorded by SAR imagers, in particular the identification and quantification of temporal changes. A systematic (re)classification shall allow... View full abstract»

• ### A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification

Publication Year: 2014, Page(s):317 - 326
Cited by:  Papers (55)
| | PDF (1923 KB) | HTML

Hyperspectral imaging fully portrays materials through numerous and contiguous spectral bands. It is a very useful technique in various fields, including astronomy, medicine, food safety, forensics, and target detection. However, hyperspectral images include redundant measurements, and most classification studies encountered the Hughes phenomenon. Finding a small subset of effective features to mo... 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 (4)
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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»

• ### Land Surface Temperature and Surface Air Temperature in Complex Terrain

Publication Year: 2015, Page(s):4762 - 4774
Cited by:  Papers (5)
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Land surface temperature (LST) is a fundamental physical property relevant to many ecological, hydrological, and atmospheric processes. There is a strong relationship between LST and near surface air temperature (Tair), although the two temperatures have different physical meaning and responses to atmospheric conditions. In complex terrain, these differences are amplified; yet it is in ... View full abstract»

• ### Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval

Publication Year: 2018, Page(s):1482 - 1493
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Leaf area index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional vegetation indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the normalized difference vegetation index (NDVI), are commonly used to estimate the LAI. However, these indices commonly saturate at moderate-to-dense canopies (e.g., NDVI saturates when LAI exc... View full abstract»

• ### Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial–Spectral Total Variation

Publication Year: 2018, Page(s):713 - 729
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Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their subsequent application. In this paper, we model the stripes, deadlines, and impulse noise as sparse noise, and propose a unified mixed Gaussian noise and sparse noise removal framework named spatial-spectral total var... View full abstract»

• ### DETER-B: The New Amazon Near Real-Time Deforestation Detection System

Publication Year: 2015, Page(s):3619 - 3628
Cited by:  Papers (7)
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The Brazilian Legal Amazon (BLA), the largest global rainforest on earth, contains nearly 30% of the rainforest on earth. Given the regional complexity and dynamics, there are large government investments focused on controlling and preventing deforestation. The National Institute for Space Research (INPE) is currently developing five complementary BLA monitoring systems, among which the near real-... View full abstract»

• ### Monitoring Line-Infrastructure With Multisensor SAR Interferometry: Products and Performance Assessment Metrics

Publication Year: 2018, Page(s):1593 - 1605
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Satellite radar interferometry (InSAR) is an emerging technique to monitor the stability and health of line-infrastructure assets, such as railways, dams, and pipelines. However, InSAR is an opportunistic approach as the location and occurrence of its measurements (coherent scatterers) cannot be guaranteed, and the quality of the InSAR products is not uniform. This is a problem for operational ass... 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 (5)
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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»

• ### A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening

Publication Year: 2018, Page(s):978 - 989
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Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multispectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly nonlinear, inspired by the powerful represen... View full abstract»

• ### DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation

Publication Year: 2018, Page(s):1 - 9
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Semantic segmentation is a fundamental research in optical remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there were a few of works using CNN for sea-land segmentation and the results could be further improved. Th... View full abstract»

• ### A Comprehensive Evaluation of Spectral Distance Functions and Metrics for Hyperspectral Image Processing

Publication Year: 2015, Page(s):3224 - 3234
Cited by:  Papers (6)
| | PDF (2036 KB) | HTML

Distance functions are at the core of important data analysis and processing tools, e.g., PCA, classification, vector median filter, and mathematical morphology. Despite its key role, a distance function is often used without careful consideration of its underlying assumptions and mathematical construction. With the objective of identifying a suitable distance function for hyperspectral images so ... View full abstract»

• ### Feature Extraction From Multitemporal SAR Images Using Selforganizing Map Clustering and Object-Based Image Analysis

Publication Year: 2018, Page(s):1556 - 1570
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We introduce a new architecture for feature extraction from multitemporal synthetic aperture radar (SAR) data. Its the purpose is to combine classic SAR processing and geographical object-based image analysis to provide a robust unsupervised tool for information extraction from time series images. The architecture takes advantage from the characteristics of the recently introduced RGB products of ... View full abstract»

• ### Regional Glacier Mapping Using Optical Satellite Data Time Series

Publication Year: 2016, Page(s):3698 - 3711
Cited by:  Papers (3)
| | PDF (6029 KB) | HTML

The first of two Sentinel-2 satellites, launched mid2015, has similar characteristics as the Landsat TM/ETM + /OLI satellites. Together, these satellites will produce a tremendous quantity of optical images worldwide for glacier mapping, with increasing temporal coverage toward the more glacierized higher latitudes due to convergence of near-polar orbits. To exploit the potential of such near-futu... 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»

• ### Classification of Urban Building Type from High Spatial Resolution Remote Sensing Imagery Using Extended MRS and Soft BP Network

Publication Year: 2017, Page(s):3515 - 3528
| | PDF (1606 KB) | HTML

This study presents a new approach for classification of building type in complex urban scene. The approach consists of two parts: extended multiresolution segmentation (EMRS) and soft classification using BP network (SBP). The technology scheme is referred to here as EMRS-SBP. EMRS is used to guide the design of descriptor. A descriptor is a feature expression or a symbolized algorithm to systema... View full abstract»

• ### Realistic Lower Bound on Elevation Estimation for Tomographic SAR

Publication Year: 2018, Page(s):1 - 11
| | PDF (1604 KB)

The noise in a tomographic synthetic aperture radar (Tomo-SAR) model is normally assumed to be independent and identically distributed (i.i.d.) Gaussian. In this paper, the correlated Tomo-SAR model is introduced by studying the effect of random residual phase and correlated additive Gaussian noise, and a realistic and general hybrid Cramér–Rao bound (HCRB) on elevation estimation is... View full abstract»

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

Publication Year: 2018, Page(s):1030 - 1040
| | PDF (813 KB) | HTML

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) image fu... View full abstract»

• ### Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks

Publication Year: 2018, Page(s):876 - 887
| | PDF (1758 KB) | HTML

Tobacco plant detection plays an important role in the management of tobacco planting. In this paper, a new algorithm based on deep neural networks is proposed to detect tobacco plants in images captured by unmanned aerial vehicles (UAVs) (called UAV images). These UAV images are characterized by a very high spatial resolution (35 mm), and consequently contain an extremely high level of detail for... View full abstract»

• ### Mapping Global Bamboo Forest Distribution Using Multisource Remote Sensing Data

Publication Year: 2018, Page(s):1458 - 1471
| | PDF (2081 KB) | HTML

Bamboo forest has great potential in climate change mitigation. However, the spatiotemporal pattern of carbon storage of global bamboo forest is still cannot be accurately estimated, because the lack of an accurate global bamboo forest distribution information. In this paper, the global bamboo forest distribution was mapped with the following steps. To begin with, training samples were obtained ba... View full abstract»

## Aims & Scope

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS) addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.

Full Aims & Scope

## Meet Our Editors

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