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1Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The
"... pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where pixels are heavily mixed. In the no pure-pixel case, a good blind HU approach to consid ..."
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pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where pixels are heavily mixed. In the no pure-pixel case, a good blind HU approach to consider is the minimum volume enclosing simplex (MVES). Empirical experience has suggested that MVES algorithms can perform well without pure pixels, although it was not totally clear why this is true from a theoretical viewpoint. This paper aims to address the latter issue. We develop an analysis framework wherein the perfect endmember identifiability of MVES is studied under the noiseless case. We prove that MVES is indeed robust against lack of pure pixels, as long as the pixels do not get too heavily mixed and too asymmetrically spread. The theoretical results are supported by numerical simulation results. Index Terms — Hyperspectral unmixing, minimum volume enclosing simplex, identifiability, convex geometry, pixel purity measure I.
, Kentaro Suzuki
"... Hyperspectral remote sensing makes it possible to obtain detailed spectral information of surface objects. Using airborne hyperspectral (HS) data acquired over Houston, Texas, USA, provided by the 2013 IEEE data fusion contest, the spectral reflectance characteristics of surface materials were inves ..."
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Hyperspectral remote sensing makes it possible to obtain detailed spectral information of surface objects. Using airborne hyperspectral (HS) data acquired over Houston, Texas, USA, provided by the 2013 IEEE data fusion contest, the spectral reflectance characteristics of surface materials were investigated. A multispectral (MS) image acquired by WorldView-2 satellite was also introduced and it was compared with the HS image. A field measurement using a handheld spectroradiometer (EKO MS-720) was also carried out by the present authors. The irradiances of surface materials obtained by the measurement were also compared with the digital numbers of the 144 HS bands. Finally supervised classification was conducted for the HS and MS data and their results were discussed.
SUPERPIXEL-BASED CLASSIFICATION OF HYPERSPECTRAL DATA USING SPARSE REPRESENTATION AND CONDITIONAL RANDOM FIELDS
"... This paper presents a superpixel-based classifier for landcover mapping of hyperspectral image data. The approach relies on the sparse representation of each pixel by a weighted linear combination of the training data. Spatial information is incor-porated by using a coarse patch-based neighborhood a ..."
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This paper presents a superpixel-based classifier for landcover mapping of hyperspectral image data. The approach relies on the sparse representation of each pixel by a weighted linear combination of the training data. Spatial information is incor-porated by using a coarse patch-based neighborhood around each pixel as well as data-adapted superpixels. The classifica-tion is done via a hierarchical conditional random field, which utilizes the sparse-representation output and models spatial and hierarchical structures in the hyperspectral image. The experiments show that the proposed approach results in supe-rior accuracies in comparison to sparse-representation based classifiers that solely use a patch-based neighborhood. Index Terms — Sparse coding, sparse representation, su-perpixel, hyperspectral, random field
Shapelet-Based Sparse Image Representation for Landcover Classification of Hyperspectral Data
"... Abstract—This paper presents a novel sparse representation-based classifier for landcover mapping of hyperspectral image data. Each image patch is factorized into segmentation patterns, also called shapelets, and patch-specific spectral features. The combination of both is represented in a patch-spe ..."
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Abstract—This paper presents a novel sparse representation-based classifier for landcover mapping of hyperspectral image data. Each image patch is factorized into segmentation patterns, also called shapelets, and patch-specific spectral features. The combination of both is represented in a patch-specific spatial-spectral dictionary, which is used for a sparse coding procedure for the reconstruction and classification of image patches. Hereby, each image patch is sparsely represented by a linear combination of elements out of the dictionary. The set of shapelets is specifically learned for each image in an unsupervised way in order to capture the image structure. The spectral features are assumed to be the training data. The experiments show that the proposed approach shows superior results in comparison to sparse-representation based classifiers that use no or only limited spatial information and behaves competitive or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse representation-based classifiers. I.
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"... Urban land-cover classification based on airborne hyperspectral data and field observation ..."
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Urban land-cover classification based on airborne hyperspectral data and field observation
A Scalable Dataflow Accelerator for Real Time
"... Abstract. Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications. Previous work has shown that Support Vector Machines (SVMs) can achieve high classification accuracy, but unfortunately it is very computationally ex-pensive.This pape ..."
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Abstract. Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications. Previous work has shown that Support Vector Machines (SVMs) can achieve high classification accuracy, but unfortunately it is very computationally ex-pensive.This paper presents a scalable dataflow accelerator on FPGA for real-time SVM classification of hyperspectral images.To address data de-pendencies, we adapt multi-class classifier based on Hamming distance. The architecture is scalable to high problem dimensionality and available hardware resources. Implementation results show that the FPGA design achieves speedups of 26x, 1335x, 66x and 14x compared with implemen-tations on ZYNQ, ARM, DSP and Xeon processors. Moreover, one to two orders of magnitude reduction in power consumption is achieved for the AVRIS hyperspectral image datasets. 1 introduction Hyperspectral image (HSI) classification aims to assign a categorical class label
ON THE SAMPLING STRATEGIES FOR EVALUATION OF JOINT SPECTRAL-SPATIAL INFORMATION BASED CLASSIFIERS
"... Joint spectral-spatial information based classification is an active topic in hyperspectral remote sensing. Current classification ap-proaches adopt a random sampling strategy to evaluate the perfor-mance of various classification systems. Due to the limitation of benchmark data, sampling of trainin ..."
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Joint spectral-spatial information based classification is an active topic in hyperspectral remote sensing. Current classification ap-proaches adopt a random sampling strategy to evaluate the perfor-mance of various classification systems. Due to the limitation of benchmark data, sampling of training and testing data is performed on the same image. In this paper, we point out that while training with random sampling is practical for hyperspectral image classi-fication, it has intrinsic problems in evaluating spectral-spatial in-formation based classifiers. This statement is supported by several experiments, and has lead to the proposal of a new sampling strategy for comparing spectral spatial information based classifiers. Index Terms — Hyperspectral classification, spectral-spatial analysis, feature extraction, sampling
Article Adjusted Spectral Matched Filter for Target Detection in Hyperspectral Imagery
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