# IEEE Transactions on Geoscience and Remote Sensing

## Filter Results

Displaying Results 1 - 25 of 30
• ### [Front cover]

Publication Year: 2010, Page(s): C1
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• ### IEEE Transactions on Geoscience and Remote Sensing publication information

Publication Year: 2010, Page(s): C2
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• ### Table of contents

Publication Year: 2010, Page(s):3869 - 3870
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• ### Foreword to the Special Issue on Hyperspectral Image and Signal Processing

Publication Year: 2010, Page(s):3871 - 3876
Cited by:  Papers (12)
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• ### End-to-End Simulation and Analytical Model of Remote-Sensing Systems: Application to CRISM

Publication Year: 2010, Page(s):3877 - 3888
Cited by:  Papers (12)
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The simulation of remote-sensing hyperspectral images is a useful tool for a variety of tasks such as the design of systems, the understanding of the image formation process, and the development and validation of data processing algorithms. The lack of ground truth and the incomplete knowledge of the Martian environment make simulation studies of Mars hyperspectral images a useful tool for automat... View full abstract»

• ### On Hyperspectral Image Simulation of a Complex Woodland Area

Publication Year: 2010, Page(s):3889 - 3902
Cited by:  Papers (3)
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Hyperspectral imaging (HSI) systems can acquire both spectral and spatial information of ground surface simultaneously and have been used in a variety of applications such as object detection, material identification, land cover classification, etc. Through simulation of a HSI process, it is in favor of finding key contributors to optimize system performance and sensor design. Although it is diffi... View full abstract»

• ### Transformation From Hyperspectral Radiance Data to Data of Other Sensors Based on Spectral Superresolution

Publication Year: 2010, Page(s):3903 - 3912
Cited by:  Papers (3)  |  Patents (1)
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Hyperspectral radiance spectra are the sensor's response, through its spectral response functions (SRFs), to the at-sensor radiance field. As the SRFs vary with sensors, hyperspectral radiance data need to be transformed for cross-calibration with another sensor or data simulation of a future sensor. In fact, the hyperspectral radiance data are composed of average radiance in the sensor's passband... View full abstract»

• ### A BOI-Preserving-Based Compression Method for Hyperspectral Images

Publication Year: 2010, Page(s):3913 - 3923
Cited by:  Papers (4)
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Hyperspectral images (HSI) regularly contain hundreds of bands, which are of different importance in the application. Most HSI compression methods usually deal with most bands in the same way, and they do not take the difference of different bands into consideration, which may cause the loss of important spectral information. In order to preserve the spectral information of interest for applicatio... View full abstract»

• ### Improved Methods for Spectral Calibration of On-Orbit Imaging Spectrometers

Publication Year: 2010, Page(s):3924 - 3931
Cited by:  Papers (9)
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Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features a... View full abstract»

• ### VIS-NIR Imaging Spectroscopy of Mercury's Surface: SIMBIO-SYS/VIHI Experiment Onboard the BepiColombo Mission

Publication Year: 2010, Page(s):3932 - 3940
Cited by:  Papers (1)
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The Visible and Infrared Hyperspectral Imager (VIHI) is one of the three optical heads of the Spectrometers and Imagers for MPO BepiColombo Integrated Observatory SYStem (SIMBIO-SYS) experiment onboard European Space Agency's BepiColombo cornerstone mission to Mercury. The other two optical heads of SIMBIO-SYS are a stereo camera and a high-resolution image camera. The experiment is designed to sc... View full abstract»

• ### Calibration of Hyperspectral Imaging Data: VIRTIS-M Onboard Venus Express

Publication Year: 2010, Page(s):3941 - 3950
Cited by:  Papers (3)
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The Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS) is flying onboard the European Space Agency mission Venus Express and orbiting around Venus since April 11, 2006, providing very valuable remote sensing data of the planet. The instrument combines a double capability: a high-resolution imaging in the visible-infrared range (0.28-5 μm) at moderate spectral resolution (VIRTIS-M ch... View full abstract»

• ### Spectral Smile Correction of CRISM/MRO Hyperspectral Images

Publication Year: 2010, Page(s):3951 - 3959
Cited by:  Papers (4)
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The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is affected by a common artifact to pushbroom-type imaging spectrometers, the so-called “spectral smile.” For this reason, the central wavelength and the width of the instrument spectral response vary according to the spatial dimension of the detector array. As a result, the spectral capabilities of CRISM get deteriorat... View full abstract»

• ### Linear Spectral Mixture Analysis Based Approaches to Estimation of Virtual Dimensionality in Hyperspectral Imagery

Publication Year: 2010, Page(s):3960 - 3979
Cited by:  Papers (12)
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Virtual dimensionality (VD) is a new concept which was originally developed for estimating the number of spectrally distinct signatures present in hyperspectral data. The effectiveness of the VD is determined by the technique used for VD estimation. This paper develops an orthogonal subspace projection (OSP) technique to estimate the VD. The idea is derived from linear spectral mixture analysis wh... View full abstract»

• ### Asymptotically CFAR-Unsupervised Target Detection and Discrimination in Hyperspectral Images With Anomalous-Component Pursuit

Publication Year: 2010, Page(s):3980 - 3991
Cited by:  Papers (10)
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This paper addresses the problem of anomaly detection in hyperspectral images. We propose and exploit a data model to establish the link between two main approaches in the area of anomaly detection, which are the hypothesis testing (HT) and projection pursuit. We show that combining these two approaches enables one to overcome some limitations of each method when taken separately. Indeed, the resu... View full abstract»

• ### Fully Constrained Linear Spectral Unmixing: Analytic Solution Using Fuzzy Sets

Publication Year: 2010, Page(s):3992 - 4002
Cited by:  Papers (18)
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The linear mixture model is a convenient way to describe image pixels as a linear combination of pure spectra - termed end-members. The fractional contribution from each end-member is calculated through inversion of the linear model. Despite the simplicity of the model, a nonnegativity constraint that is imposed on the fractions leads to an unmixing problem for which it is hard to find a closed an... View full abstract»

• ### Implementation Strategies for Hyperspectral Unmixing Using Bayesian Source Separation

Publication Year: 2010, Page(s):4003 - 4013
Cited by:  Papers (25)
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Bayesian positive source separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical nonnegativity of spectra and abundances has to be ensured, such as in remote sensing. Moreover, it is sensible to impose a sum-to-one (full additivity) constraint to the estimated source abundances in each pixel. Even though nonnegativity and full additivity are two necessa... View full abstract»

• ### Merging the Minnaert-$k$ Parameter With Spectral Unmixing to Map Forest Heterogeneity With CHRIS/PROBA Data

Publication Year: 2010, Page(s):4014 - 4022
Cited by:  Papers (8)
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The Compact High Resolution Imaging Spectrometer (CHRIS) mounted onboard the Project for Onboard Autonomy (PROBA) spacecraft is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with high spatial resolution. We combined the spectral domain with the angular domain of CHRIS data in order to map the surface heterogeneity of... View full abstract»

• ### Superpixel Endmember Detection

Publication Year: 2010, Page(s):4023 - 4033
Cited by:  Papers (33)
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Superpixels are homogeneous image regions comprised of multiple contiguous pixels. Superpixel representations can reduce noise in hyperspectral images by exploiting the spatial contiguity of scene features. This paper combines superpixels with endmember extraction to produce concise mineralogical summaries that assist in browsing large image catalogs. First, a graph-based agglomerative algorithm o... View full abstract»

• ### Double Nearest Proportion Feature Extraction for Hyperspectral-Image Classification

Publication Year: 2010, Page(s):4034 - 4046
Cited by:  Papers (23)
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For the classification among different land-cover types in a hyperspectral image, particularly in the small-sample-size situation, a feature-extraction method is an approach for reducing the dimensionality and increasing the classification accuracy. Fisher's linear discriminant analysis (LDA) is one of the most popular feature-extraction methods. However, it cannot be applied directly to the class... View full abstract»

• ### Decision-Level Fusion of Spectral Reflectance and Derivative Information for Robust Hyperspectral Land Cover Classification

Publication Year: 2010, Page(s):4047 - 4058
Cited by:  Papers (19)
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The developments in sensor technology have made the high-resolution hyperspectral remote sensing data available to the remote sensing analyst for ground-cover classification and target recognition tasks. The inherent high dimensionality of such data sets and the limited ground-truth data availability in many real-life operating scenarios necessitate such hyperspectral classification systems to emp... View full abstract»

• ### Automated Labeling of Materials in Hyperspectral Imagery

Publication Year: 2010, Page(s):4059 - 4070
Cited by:  Papers (12)  |  Patents (2)
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We present a technique for automatically labeling segmented hyperspectral imagery with semantically meaningful material labels. The technique compares the mean signatures of each image segment to a spectral library of known materials, and material labels are assigned to image segments according to the most similar library entry. The similarity between spectral signatures is evaluated using our rec... View full abstract»

• ### Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification

Publication Year: 2010, Page(s):4071 - 4084
Cited by:  Papers (40)
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This paper presents the utilization of empirical mode decomposition (EMD) of hyperspectral images to increase the classification accuracy using support vector machine (SVM)-based classification. EMD has been shown in the literature to be particularly suitable for nonlinear and nonstationary signals and is used in this paper to decompose hyperspectral image bands into several intrinsic mode functio... View full abstract»

• ### Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning

Publication Year: 2010, Page(s):4085 - 4098
Cited by:  Papers (124)  |  Patents (1)
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This paper presents a new semisupervised segmentation algorithm, suited to high-dimensional data, of which remotely sensed hyperspectral image data sets are an example. The algorithm implements two main steps: 1) semisupervised learning of the posterior class distributions followed by 2) segmentation, which infers an image of class labels from a posterior distribution built on the learned class di... View full abstract»

• ### Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification

Publication Year: 2010, Page(s):4099 - 4109
Cited by:  Papers (23)
| | PDF (1975 KB) | HTML

Approaches to combine local manifold learning (LML) and the k -nearest-neighbor (kNN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and kNN, a new SLML-weighted kNN (SLML-W kNN) classifier is proposed. This method is appealing as it does not require dimensionality reduction and only depends on the weights provi... View full abstract»

• ### Adaptive Classification for Hyperspectral Image Data Using Manifold Regularization Kernel Machines

Publication Year: 2010, Page(s):4110 - 4121
Cited by:  Papers (33)
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Localized training data typically utilized to develop a classifier may not be fully representative of class signatures over large areas but could potentially provide useful information which can be updated to reflect local conditions in other areas. An adaptive classification framework is proposed for this purpose, whereby a kernel machine is first trained with labeled data and then iteratively ad... View full abstract»

## Aims & Scope

IEEE Transactions on Geoscience and Remote Sensing (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Antonio J. Plaza
University of Extremadura