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

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Displaying Results 1 - 25 of 42
• ### Front cover

Publication Year: 2014, Page(s): C1
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• ### IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing publication information

Publication Year: 2014, Page(s): C2
| PDF (137 KB)

Publication Year: 2014, Page(s):1005 - 1006
| PDF (133 KB)
• ### Foreword to the Special Issue on Machine Learning for Remote Sensing Data Processing

Publication Year: 2014, Page(s):1007 - 1011
Cited by:  Papers (6)
| PDF (198 KB) | HTML
• ### Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification

Publication Year: 2014, Page(s):1012 - 1022
Cited by:  Papers (26)
| | PDF (2520 KB) | HTML

By coupling the nearest-subspace classification with a distance-weighted Tikhonov regularization, nearest regularized subspace (NRS) was recently developed for hyperspectral image classification. However, the NRS was originally designed to be a pixel-wise classifier which considers the spectral signature only while ignoring the spatial information at neighboring locations. Gabor features have curr... View full abstract»

• ### A Two-Stage Feature Selection Framework for Hyperspectral Image Classification Using Few Labeled Samples

Publication Year: 2014, Page(s):1023 - 1035
Cited by:  Papers (15)
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Although the high dimensionality of hyperspectral data increases the separability of land covers, it is difficult to distinguish certain classes using only the spectral information due to the widespread mixed pixels and small sample size problems. Three-dimensional Gabor wavelet transform takes the entire hyperspectral data cube as a tensor, captures the joint spectral-spatial structures very well... View full abstract»

• ### A Batch-Mode Active Learning Algorithm Using Region-Partitioning Diversity for SVM Classifier

Publication Year: 2014, Page(s):1036 - 1046
Cited by:  Papers (5)
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In this paper, a region-partitioning active learning (AL) technique is proposed for classification of remote sensing (RS) images based on the support vector machines (SVM) classifier. In the batch-mode AL process, diversity information is required to select a batch of informative samples. A new AL technique that aims to introduce diversity information is proposed based on relative positions of can... View full abstract»

• ### Spectral–Spatial Preprocessing Using Multihypothesis Prediction for Noise-Robust Hyperspectral Image Classification

Publication Year: 2014, Page(s):1047 - 1059
Cited by:  Papers (24)
| | PDF (3377 KB) | HTML

Spectral-spatial preprocessing using multihypothesis prediction is proposed for improving accuracy of hyperspectral image classification. Specifically, multiple spatially collocated pixel vectors are used as a hypothesis set from which a prediction for each pixel vector of interest is generated. Additionally, a spectral-band-partitioning strategy based on inter-band correlation coefficients is pro... View full abstract»

• ### ${{rm E}^{2}}{rm LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification

Publication Year: 2014, Page(s):1060 - 1069
Cited by:  Papers (48)
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Extreme learning machine (ELM) has attracted attentions in pattern recognition field due to its remarkable advantages such as fast operation, straightforward solution, and strong generalization. However, the performance of ELM for high-dimensional data, such as hyperspectral image, is still an open problem. Therefore, in this paper, we introduce ELM for hyperspectral image classification. Furtherm... View full abstract»

• ### PerTurbo Manifold Learning Algorithm for Weakly Labeled Hyperspectral Image Classification

Publication Year: 2014, Page(s):1070 - 1078
Cited by:  Papers (4)
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Hyperspectral data analysis has been given a growing attention due to the scientific challenges it raises and the wide set of applications that can benefit from it. Classification of hyperspectral images has been identified as one of the hottest topics in this context, and has been mainly addressed by discriminative methods such as SVM. In this paper, we argue that generative methods, and especial... View full abstract»

• ### An Evaluation of Low-Rank Mahalanobis Metric Learning Techniques for Hyperspectral Image Classification

Publication Year: 2014, Page(s):1079 - 1088
Cited by:  Papers (7)
| | PDF (1312 KB) | HTML

We provide a comparative study of several state-of-the-art Mahalanobis metric learning algorithms evaluated on three well-studied, high-dimensional hyperspectral images captured by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument. We focus on the problem of low-rank Mahalanobis metric learning, where our objective is to learn an n × m projection matrix A, where m ... View full abstract»

• ### Ensemble Learning in Hyperspectral Image Classification: Toward Selecting a Favorable Bias-Variance Tradeoff

Publication Year: 2014, Page(s):1089 - 1102
Cited by:  Papers (8)
| | PDF (2173 KB) | HTML

Automated classification of hyperspectral images is a fast growing field with numerous applications in the areas of security and surveillance, agriculture, urban management, and environmental monitoring. Although significant progress has been achieved in various aspects of hyperspectral classification (e.g., feature extraction, feature selection, classification, and post-classification processing)... View full abstract»

• ### Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

Publication Year: 2014, Page(s):1103 - 1115
Cited by:  Papers (5)
| | PDF (2025 KB) | HTML

In the past few years, segmentation and classification techniques have become a cornerstone of many successful remote sensing algorithms aiming at delineating geographic target objects. One common strategy relies on using multiple complex features to guide the delineation process with the objective of gathering complementary information for improving classification results. However, a persistent p... View full abstract»

• ### SAR Image Classification Through Information-Theoretic Textural Features, MRF Segmentation, and Object-Oriented Learning Vector Quantization

Publication Year: 2014, Page(s):1116 - 1126
Cited by:  Papers (13)
| | PDF (2552 KB) | HTML

Segmentation of optical images may be obtained through algorithms based on image prior models that exploit the spatial dependencies of land covers. In synthetic aperture radar (SAR) images, speckle conceals such spatial dependencies and segmentation algorithms suitable for optical images may become ineffective. Textural features may be used to emphasize spatial dependencies in the data and hence t... View full abstract»

• ### Pattern Retrieval in Large Image Databases Using Multiscale Coarse-to-Fine Cascaded Active Learning

Publication Year: 2014, Page(s):1127 - 1141
Cited by:  Papers (12)
| | PDF (2140 KB) | HTML

Pattern retrieval is a fundamental challenge in machine learning but is often subject to the problem of gathering enough labeled examples of the target pattern, and also to the computational complexity inherent to the training and the evaluation of complex classifier functions on large databases. In this paper, we propose a hierarchical top-down processing scheme for pattern retrieval in high-volu... View full abstract»

• ### Phenology-Driven Land Cover Classification and Trend Analysis Based on Long-term Remote Sensing Image Series

Publication Year: 2014, Page(s):1142 - 1156
Cited by:  Papers (8)
| | PDF (3464 KB) | HTML

The objective of this study is to classify the land cover types and analyze the land cover trend by incorporating phenological variability throughout a range of natural ecosystems using time-series remotely sensed images. First, a breaks for additive seasonal and trend (BFAST) approach is used to extract the phenology information from the time series. Second, a dynamic time warping (DTW) approach ... View full abstract»

• ### The Synergy of the $0.05^circ$ ($sim5nbsphbox{km}$ ) AVHRR Long-Term Data Record (LTDR) and Landsat TM Archive to Map Large Fires in the North American Boreal Region From 1984 to 1998

Publication Year: 2014, Page(s):1157 - 1166
Cited by:  Papers (1)
| | PDF (1948 KB) | HTML

A Bayesian network classifier-based algorithm was applied to map the burned area (BA) in the North American boreal region using the 0.05° ( ~ 5nbspkm) Advanced Very High Resolution Radiometer (AVHRR) Long-Term Data Record (LTDR) data version 3 time series. The results showed an overall good agreement compared to reference maps ( slope = 0.62; R2 = 0.75). The study site was divide... View full abstract»

• ### A Generic Land-Cover Classification Framework for Polarimetric SAR Images Using the Optimum Touzi Decomposition Parameter Subset—An Insight on Mutual Information-Based Feature Selection Techniques

Publication Year: 2014, Page(s):1167 - 1176
Cited by:  Papers (7)
| | PDF (2399 KB) | HTML

This correspondence proposes a generic framework for land-cover classification using support vector machine (SVM) classifier for polarimetric synthetic aperture radar (SAR) images considering the optimum Touzi decomposition parameters. Some new concerns have been raised recently with the Cloude-Pottier decomposition. Cloude's α scattering type ambiguities may take place for certain scattere... View full abstract»

• ### Spectral–Spatial Classification of Hyperspectral Images Using Wavelets and Extended Morphological Profiles

Publication Year: 2014, Page(s):1177 - 1185
Cited by:  Papers (19)
| | PDF (1321 KB) | HTML

This paper deals with hyperspectral image classification in remote sensing. The proposed scheme is a spectral-spatial technique based on wavelet transforms and mathematical morphology. The original contribution of this paper is that the extended morphological profile (EMP) is created from the features extracted by wavelets, which has proven to be better or comparable to other techniques for dimens... View full abstract»

• ### Comparative Assessment of Supervised Classifiers for Land Use–Land Cover Classification in a Tropical Region Using Time-Series PALSAR Mosaic Data

Publication Year: 2014, Page(s):1186 - 1199
Cited by:  Papers (14)
| | PDF (4198 KB) | HTML

Numerous classification algorithms have been proposed to create accurate classification maps using optical remote sensing data. However, few comparative studies evaluate the performance of classification algorithms with focus on tropical forests due to cloud effects. Advances in synthetic aperture radar (SAR) techniques and spatial resolution, mapping, and comparison of classification algorithms a... View full abstract»

• ### A Neural Approach Under Active Learning Mode for Change Detection in Remotely Sensed Images

Publication Year: 2014, Page(s):1200 - 1206
Cited by:  Papers (3)
| | PDF (838 KB) | HTML

In this paper, a change detection technique using neural networks in active learning framework is proposed under the scarcity of labeled patterns. In the present investigation, two variants of radial basis function neural networks and a multilayer perceptron are used as learners. Instead of training the network (or ensemble of networks) with randomly collected labeled patterns, in the proposed wor... View full abstract»

• ### Extraction of Built-up Areas From Fully Polarimetric SAR Imagery Via PU Learning

Publication Year: 2014, Page(s):1207 - 1216
Cited by:  Papers (7)
| | PDF (1718 KB) | HTML

In this paper, we propose a PU learning (i.e., learning from positive and unlabeled data, which trains a binary classifier using only PU examples) based method for extracting the built-up areas (BAs) from fully polarimetric synthetic aperture radar (PolSAR) imagery. The key feature is that there are no labeled negative training data, thus the traditional classification techniques are not applicabl... View full abstract»

• ### Optimal Sparse Kernel Learning in the Empirical Kernel Feature Space for Hyperspectral Classification

Publication Year: 2014, Page(s):1217 - 1226
Cited by:  Papers (2)
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In this paper, a novel framework for optimal sparse kernel learning for support vector machine (SVM) classifier in a finite-dimensional space called the empirical kernel feature space (EKFS) is presented. In conventional sparse kernel learning techniques, feature selection algorithms are optimal up to linear kernel because the contributions of individual features in the input space to the margin o... View full abstract»

• ### A Robust Nonlinear Hyperspectral Anomaly Detection Approach

Publication Year: 2014, Page(s):1227 - 1234
Cited by:  Papers (18)
| | PDF (1472 KB) | HTML

This paper proposes a nonlinear version of an anomaly detector with a robust regression detection strategy for hyperspectral imagery. In the traditional Mahalanobis distance-based hyperspectral anomaly detectors, the background statistics are easily contaminated by anomaly targets, resulting in a poor detection performance. The traditional detectors also often fail to detect anomaly targets when t... View full abstract»

• ### An Adaptive Memetic Fuzzy Clustering Algorithm With Spatial Information for Remote Sensing Imagery

Publication Year: 2014, Page(s):1235 - 1248
Cited by:  Papers (14)
| | PDF (3081 KB) | HTML

Due to its inherent complexity, remote sensing image clustering is a challenging task. Recently, some spatial-based clustering approaches have been proposed; however, one crucial factor with regard to their clustering quality is that there is usually one parameter that controls their spatial information weight, which is difficult to determine. Meanwhile, the traditional optimization methods of the... 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