# IEEE Transactions on Geoscience and Remote Sensing

## Volume 49 Issue 11  Part 1 • Nov. 2011

This issue contains several parts.Go to:  Part 2

## Filter Results

Displaying Results 1 - 25 of 28
• ### [Front cover]

Publication Year: 2011, Page(s): C1
| PDF (286 KB)
• ### IEEE Transactions on Geoscience and Remote Sensing publication information

Publication Year: 2011, Page(s): C2
| PDF (41 KB)

Publication Year: 2011, Page(s):4101 - 4102
| PDF (50 KB)
• ### Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data

Publication Year: 2011, Page(s):4103 - 4110
Cited by:  Papers (71)
| PDF (201 KB) | HTML
• ### List of reviewers

Publication Year: 2011, Page(s): 4111
| PDF (15 KB)
• ### Fully Constrained Least Squares Spectral Unmixing by Simplex Projection

Publication Year: 2011, Page(s):4112 - 4122
Cited by:  Papers (82)
| | PDF (385 KB) | HTML

We present a new algorithm for linear spectral mixture analysis, which is capable of supervised unmixing of hyperspectral data while respecting the constraints on the abundance coefficients. This simplex-projection unmixing algorithm is based upon the equivalence of the fully constrained least squares problem and the problem of projecting a point onto a simplex. We introduce several geometrical pr... View full abstract»

• ### Component Analysis-Based Unsupervised Linear Spectral Mixture Analysis for Hyperspectral Imagery

Publication Year: 2011, Page(s):4123 - 4137
Cited by:  Papers (40)
| | PDF (1743 KB) | HTML

Two of the most challenging issues in the unsupervised linear spectral mixture analysis (ULSMA) are: 1) determining the number of signatures to form a linear mixing model; and 2) finding the signatures used to unmix data. These two issues do not occur in supervised LSMA since the target signatures are assumed to be known a priori. With recent advances in hyperspectral sensor technology, many unkno... View full abstract»

• ### Analysis of Imaging Spectrometer Data Using$N$-Dimensional Geometry and a Mixture-Tuned Matched Filtering Approach

Publication Year: 2011, Page(s):4138 - 4152
Cited by:  Papers (61)
| | PDF (2021 KB) | HTML

Imaging spectrometers collect unique data sets that are simultaneously a stack of spectral images and a spectrum for each image pixel. While these data can be analyzed using approaches designed for multispectral images, or alternatively by looking at individual spectra, neither of these takes full advantage of the dimensionality of the data. Imaging spectrometer spectral radiance data or derived a... View full abstract»

• ### Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model

Publication Year: 2011, Page(s):4153 - 4162
Cited by:  Papers (175)
| | PDF (731 KB) | HTML

Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of the accepted linear mixing model but also of a bilinear model that has been recently introduced in the literature. Appropriate priors are chosen ... View full abstract»

• ### Pixel Unmixing in Hyperspectral Data by Means of Neural Networks

Publication Year: 2011, Page(s):4163 - 4172
Cited by:  Papers (55)
| | PDF (1532 KB) | HTML

Neural networks (NNs) are recognized as very effective techniques when facing complex retrieval tasks in remote sensing. In this paper, the potential of NNs has been applied in solving the unmixing problem in hyperspectral data. In its complete form, the processing scheme uses an NN architecture consisting of two stages: the first stage reduces the dimension of the input vector, while the second s... View full abstract»

• ### Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Discrete Particle Swarm Optimization Algorithm

Publication Year: 2011, Page(s):4173 - 4176
Cited by:  Papers (36)
| | PDF (257 KB) | HTML

This paper described endmember extraction as a combinatorial optimization problem (COP). By defining particles' position and velocity, discrete particle swarm optimization (D-PSO) was proposed based on particle swarm optimization to resolve COP. The algorithm was tested and evaluated by hyperspectral remote sensing data. Experimental results showed that, while extracting the same number of endmemb... View full abstract»

• ### A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction

Publication Year: 2011, Page(s):4177 - 4193
Cited by:  Papers (86)
| | PDF (1188 KB) | HTML

In the late 1990s, Winter proposed an endmember extraction belief that has much impact on endmember extraction techniques in hyperspectral remote sensing. The idea is to find a maximum-volume simplex whose vertices are drawn from the pixel vectors. Winter's belief has stimulated much interest, resulting in many different variations of pixel search algorithms, widely known as N-FINDR, being propose... View full abstract»

• ### Chance-Constrained Robust Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing

Publication Year: 2011, Page(s):4194 - 4209
Cited by:  Papers (48)
| | PDF (1173 KB) | HTML

Effective unmixing of hyperspectral data cube under a noisy scenario has been a challenging research problem in remote sensing arena. A branch of existing hyperspectral unmixing algorithms is based on Craig's criterion, which states that the vertices of the minimum-volume simplex enclosing the hyperspectral data should yield high fidelity estimates of the endmember signatures associated with the d... View full abstract»

• ### Improving Spatial–Spectral Endmember Extraction in the Presence of Anomalous Ground Objects

Publication Year: 2011, Page(s):4210 - 4222
Cited by:  Papers (14)
| | PDF (1469 KB) | HTML

Endmember extraction (EE) has been widely utilized to extract spectrally unique and singular spectral signatures for spectral mixture analysis of hyperspectral images. Recently, spatial-spectral EE (SSEE) algorithms have been proposed to achieve superior performance over spectral EE (SEE) algorithms by taking both spectral similarity and spatial context into account. However, these algorithms tend... View full abstract»

• ### A Hybrid Automatic Endmember Extraction Algorithm Based on a Local Window

Publication Year: 2011, Page(s):4223 - 4238
Cited by:  Papers (35)
| | PDF (3457 KB) | HTML

Anomaly endmembers play an important role in the application of remote sensing, such as in unmixing classification and target detection. Inspired by the iterative error analysis (IEA), a hybrid endmember extraction algorithm (HEEA) based on a local window is proposed in this paper, which focuses on improving the accuracy of endmember extraction. HEEA uses the spectral-information-divergence-spectr... View full abstract»

• ### Enhancing Hyperspectral Image Unmixing With Spatial Correlations

Publication Year: 2011, Page(s):4239 - 4247
Cited by:  Papers (74)
| | PDF (778 KB) | HTML

This paper describes a new algorithm for hyperspectral image unmixing. Most unmixing algorithms proposed in the literature do not take into account the possible spatial correlations between the pixels. In this paper, a Bayesian model is introduced to exploit these correlations. The image to be unmixed is assumed to be partitioned into regions (or classes) where the statistical properties of the ab... View full abstract»

• ### Spatially Adaptive Hyperspectral Unmixing

Publication Year: 2011, Page(s):4248 - 4262
Cited by:  Papers (31)
| | PDF (2463 KB) | HTML

Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing process. However, many of these methods rely on usin... View full abstract»

• ### Learning Discriminative Sparse Representations for Modeling, Source Separation, and Mapping of Hyperspectral Imagery

Publication Year: 2011, Page(s):4263 - 4281
Cited by:  Papers (79)
| | PDF (1791 KB) | HTML

A method is presented for subpixel modeling, mapping, and classification in hyperspectral imagery using learned block-structured discriminative dictionaries, where each block is adapted and optimized to represent a material in a compact and sparse manner. The spectral pixels are modeled by linear combinations of subspaces defined by the learned dictionary atoms, allowing for linear mixture analysi... View full abstract»

• ### Hyperspectral Unmixing via$L_{1/2}$Sparsity-Constrained Nonnegative Matrix Factorization

Publication Year: 2011, Page(s):4282 - 4297
Cited by:  Papers (166)
| | PDF (1877 KB) | HTML

Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of theL1regularizer. Unfortunate... View full abstract»

• ### Pixel-Unmixing Moderate-Resolution Remote Sensing Imagery Using Pairwise Coupling Support Vector Machines: A Case Study

Publication Year: 2011, Page(s):4298 - 4307
Cited by:  Papers (8)
| | PDF (1066 KB) | HTML

A method combined with support vector machines (SVMs) and pairwise coupling (PWC) was developed to achieve land use/land cover fractions of a moderate-resolution remote sensing image. At first, SVMs were applied to solve classification problems. Then, they were extended with PWC to output probabilities as the abundance of landscape fractions. The performances were evaluated by using the “estimated... View full abstract»

• ### Multitemporal Unmixing of Medium-Spatial-Resolution Satellite Images: A Case Study Using MERIS Images for Land-Cover Mapping

Publication Year: 2011, Page(s):4308 - 4317
Cited by:  Papers (15)
| | PDF (1057 KB) | HTML

Data from current medium-spatial-resolution imaging spectroradiometers are used for land-cover mapping and land-cover change detection at regional to global scales. However, few landscapes are homogeneous at these scales, and this creates the so-called mixed-pixel problem. In this context, this study explores the use of the linear spectral mixture model to extract subpixel land-cover composition f... View full abstract»

• ### SVM-Based Unmixing-to-Classification Conversion for Hyperspectral Abundance Quantification

Publication Year: 2011, Page(s):4318 - 4327
Cited by:  Papers (16)
| | PDF (1100 KB) | HTML

Need for a priori knowledge of the components comprising each pixel in a scene has set the endmember determination, rather than the endmember abundance quantification, as the primary focus of many unmixing approaches. In the absence of the information about the pure signatures present in an image scene, which is often the case, the mean spectra of the pixel vectors, directly extracted from the sce... View full abstract»

• ### Retrieval of Canopy Closure and LAI of Moso Bamboo Forest Using Spectral Mixture Analysis Based on Real Scenario Simulation

Publication Year: 2011, Page(s):4328 - 4340
Cited by:  Papers (4)
| | PDF (1789 KB) | HTML

This paper investigates the retrievals of the canopy closure and leaf area index (LAI) of the Moso bamboo forest from the Landsat Thematic Mapper data using a constrained linear spectral unmixing method. A new approach for endmember collection based on the real scenario simulation of the Moso bamboo forest is developed. Four fraction images (i.e., sunlit canopy, shaded canopy, sunlit background, a... View full abstract»

• ### Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study

Publication Year: 2011, Page(s):4341 - 4358
Cited by:  Papers (18)
| | PDF (2234 KB) | HTML

As the volume of hyperspectral data for planetary exploration increases, efficient yet accurate algorithms are decisive for their analysis. In this paper, the capability of spectral unmixing for analyzing hyperspectral images from Mars is investigated. For that purpose, we consider the Russell megadune observed by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) and the High-Resolu... View full abstract»

Publication Year: 2011, Page(s): 4359
| PDF (223 KB)

## Aims & Scope

IEEE Transactions on Geoscience and Remote Sensing (TGRS) s 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. This journal publishes technical papers disclosing new and significant research.  The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Simon H. Yueh
Jet Propulsion Laboratory