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

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• ### 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»

• ### 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»

• ### 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»

• ### 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»

• ### 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»

• ### 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»

• ### 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»

• ### 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»

• ### 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 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»

• ### 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»

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

Publication Year: 2018, Page(s):1715 - 1724
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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»

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

Publication Year: 2018, Page(s):1227 - 1243
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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»

• ### 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»

• ### 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»

• ### 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»

• ### 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»

• ### 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»

• ### 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»

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

Publication Year: 2018, Page(s):876 - 887
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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»

• ### 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»

• ### 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»

• ### 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»

• ### 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»

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

Publication Year: 2017, Page(s):5322 - 5328
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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»

• ### 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»

• ### 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»

• ### Polarimetric SAR Image Classification Using Geodesic Distances and Composite Kernels

Publication Year: 2018, Page(s):1606 - 1614
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The covariance/coherence matrices are the most common way of representing polarimetric information in the polarimetric synthetic aperture radar (PolSAR) data and have been extensively used in PolSAR classification. Since PolSAR covariance and coherence matrices are Hermitian positive-definite, they form a nonlinear manifold, rather than Euclidean space. Though the geodesic distance measures define... View full abstract»

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

Publication Year: 2018, Page(s):1030 - 1040
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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»

• ### 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»

• ### Hyperspectral Image Superresolution by Transfer Learning

Publication Year: 2017, Page(s):1963 - 1974
Cited by:  Papers (5)
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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»

• ### 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»

• ### Cascaded Random Forest for Hyperspectral Image Classification

Publication Year: 2018, Page(s):1082 - 1094
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This paper proposes a Cascaded Random Forest (CRF) method, which can improve the classification performance by means of combining two different enhancements into the Random Forest (RF) algorithm. In detail, on the one hand, a neighborhood rough sets based Hierarchical Random Subspace Method is designed for feature selection, which can improve the strength of base classifiers and increase the diver... 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»

• ### 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»

• ### Application of Crop Model Data Assimilation With a Particle Filter for Estimating Regional Winter Wheat Yields

Publication Year: 2014, Page(s):4422 - 4431
Cited by:  Papers (9)
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To improve the performance of crop models for regional crop yield estimates, a particle filter (PF) was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)-Wheat model. Two experiments involving winter wheat yield estimations were conducted at a field plot and on a regional scale to test the feasibility of the PF-based data assimilation strategy... View full abstract»

• ### Snow Cover Mapping for Complex Mountainous Forested Environments Based on a Multi-Index Technique

Publication Year: 2018, Page(s):1433 - 1441
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Seasonal snow cover is a critical component of the energy and water budgets of mountainous watersheds. Capturing the snow cover in complex environments is crucial for monitoring and understanding the temporal and spatial effects of climate change on alpine snow cover. The normalized difference snow index (NDSI) can be used to effectively and accurately estimate snow cover information from satellit... 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»

• ### 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»

• ### 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»

• ### Land Subsidence in Taiyuan, China, Monitored by InSAR Technique With Multisensor SAR Datasets From 1992 to 2015

Publication Year: 2018, Page(s):1509 - 1519
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Taiyuan city has been suffering significant subsidence during last two to three decades, mainly due to the effects of groundwater withdrawal and urban construction. The purpose of this study is to map the spatial-temporal variations of land subsidence over Taiyuan and analyze the causes of the observed deformations by using the interferometric point target analysis (IPTA) technique with multisenso... 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
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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»

• ### A Deep Neural Networks Approach to Automatic Recognition Systems for Volcano-Seismic Events

Publication Year: 2018, Page(s):1533 - 1544
| | PDF (1004 KB) | HTML

Deep neural networks (DNNs) could help to identify the internal sources of volcano-seismic events. However, direct applications of DNNs are challenging, given the multiple seismic sources and the small size of available datasets. In this paper, we propose a novel approach in the field of volcano seismology to classify volcano-seismic events based on fully connected DNNs. Two DNN architectures with... View full abstract»

• ### The Total Electron Content From InSAR and GNSS: A Midlatitude Study

Publication Year: 2018, Page(s):1725 - 1733
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The total electron content (TEC) measured from the interferometric synthetic aperture radar (InSAR) and from a dense network of global navigation satellite system (GNSS) receivers are used to assess the capability of InSAR to retrieve ionospheric information, when the tropospheric contribution to the interferometric phase is reasonably negligible. With this aim, we select three night-time case stu... View full abstract»

• ### Compressed Sensing Reconstruction of Hyperspectral Images Based on Spectral Unmixing

Publication Year: 2018, Page(s):1266 - 1284
| | PDF (2216 KB) | HTML

How to utilize the characteristics of hyperspectral images (HSIs) is a key problem in application of compressed sensing theory to hyperspectral image compression and reconstruction. Based on the study of spectral mixing characteristics, a compressed sensing reconstruction algorithm with spectral unmixing for HSIs is proposed. Taking advantage of linear mixing model, the HSIs are separated into end... View full abstract»

• ### Efficient InSAR Phase Noise Reduction via Compressive Sensing in the Complex Domain

Publication Year: 2018, Page(s):1615 - 1632
| | PDF (2474 KB) | HTML

Two novel phase noise filtering algorithms for interferometric synthetic aperture radar (InSAR) are presented in this paper. Aiming at the nonlocal high self-similarity existing in the InSAR phase, we establish the phase noise filtering formulations with the ℓ0-norm regularizer and the ℓ1-norm regularizer, respectively. Although these two original formulations a... 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»

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

Publication Year: 2017, Page(s):4002 - 4009
| | PDF (890 KB) | HTML

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 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»

## 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.

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## Meet Our Editors

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