Proceedings ArticleDOI
Endmember extraction using a novel Cluster-based Spatial Border Removal Preprocessor
Fatemeh Kowkabi,Hassan Ghassemian,Ahmad Keshavarz +2 more
- pp 5047-5050
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TLDR
A novel Cluster-based Spatial Border Removal Preprocessor (CSBRP) is proposed by removing mixed pixels located at spatial borders of cluster map and calculate spectral purity weight of residual pixels in order to look for spectrally pure pixels thorough them so that the best pure pixels are adopted for the next EE stage.Abstract:
Most algorithms applied for extracting endmembers utilize spectral content of pixels with inattentive to spatial arrangement between them. Recently Spatial Spectral Preprocessor (SSPP) has been proposed for solving this problem. In this paper, we propose a novel Cluster-based Spatial Border Removal Preprocessor (CSBRP) by removing mixed pixels located at spatial borders of cluster map and calculate spectral purity weight of residual pixels in order to look for spectrally pure pixels thorough them so that the best pure pixels are adopted for the next EE stage. The performance of our method is appraised on a synthetic image derived by Rterrain HYDICE dataset and USGS library from the viewpoints of RMSE reconstruction, average minimum SAD and total processing time. Results relatively outperform the state-of-the-art techniques especially in low signal to noise ratio.read more
Citations
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Journal ArticleDOI
Hyperspectral unmixing employing l1−l2 sparsity and total variation regularization
TL;DR: A novel linear hyperspectral unmixing method based on l1−l2 sparsity and total variation (TV) regularization based on the extended alternating direction method of multipliers (ADMM) is utilized to solve the proposed model.
Journal ArticleDOI
Enhancing Hyperspectral Endmember Extraction Using Clustering and Oversegmentation-Based Preprocessing
TL;DR: An appropriate clustering and oversegmentation-based preprocessing (COPP) by greatly benefiting from the integration of spatial and spectral information is proposed and a novel top-down overse segmentation (TDOS) algorithm is developed which can recognize small oversegments with high spatial correlation.
Journal ArticleDOI
A Fast Spatial–Spectral Preprocessing Module for Hyperspectral Endmember Extraction
TL;DR: A fast spatial-spectral preprocessing module is proposed, which determines the spectral purity score of pixels located at spatially homogenous regions, which demonstrates its worthy performance in terms of accuracy and fast computation time.
Journal ArticleDOI
Hybrid Preprocessing Algorithm for Endmember Extraction Using Clustering, Over-Segmentation, and Local Entropy Criterion
TL;DR: A top-down over-segmentation algorithm in combination with fuzzy c-means (FCM) clustering to identify spatially homogenous over-Segments with minimum spectral variability and high spatial correlation to speed up EM extraction.
Proceedings ArticleDOI
Hyperspectral endmember extraction and unmixing by a novel spatial-spectral preprocessing module
TL;DR: An autonomous preprocessing module using incorporation of a novel over-segmentation and unsupervised k-means clustering algorithms that can generate spatially small and homogenous regions with high spatial correlation and minimum local spectral variability is proposed.
References
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Journal ArticleDOI
Vertex component analysis: a fast algorithm to unmix hyperspectral data
TL;DR: A new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA), which competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Journal ArticleDOI
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Jose M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul D. Gader,Jocelyn Chanussot +6 more
TL;DR: This paper presents an overview of un Mixing methods from the time of Keshava and Mustard's unmixing tutorial to the present, including Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixed algorithms.
Journal Article
Spectral unmixing
Nirmal Keshava,John F. Mustard +1 more
TL;DR: The outputs of spectral unmixing, endmember, and abundance estimates are important for identifying the material composition of mixtures and the applicability of models and techniques is highly dependent on the variety of circumstances and factors that give rise to mixed pixels.
Posted Content
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Jose M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul D. Gader,Jocelyn Chanussot +6 more
TL;DR: An overview of unmixing methods from the time of Keshava and Mustard's tutorial as mentioned in this paper to the present can be found in Section 2.2.1].
Journal ArticleDOI
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery
D.C. Heinz,Chein-I-Chang +1 more
TL;DR: The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification, where no closed form can be derived for this method and an efficient algorithm is developed to yield optimal solutions.