Comparison of hyperspectral endmember extraction algorithms

Wu, Jee-Cheng; Tsuei, Gwo-Chyang

doi:10.1117/1.jrs.7.073525

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…The proposed methodological framework for improved decision tree classification ( Figure 2) includes, mixed pixel decomposition, construction of an improved decision tree feature dataset, training sample selection based on three-dimensional (3D) Terrain, implementation of an improved decision tree, and accuracy evaluation. The basic premise of building an improved decision tree model is that (1) mixed pixel decomposition can be used to extract different endmember abundance quantities (the proportion of different kinds of features) from multispectral or hyperspectral data [29][30][31][32][33], (2) pixel unmixing can combine with classifier [34], and (3) the decision tree classification method can fuse various data features (such as terrain, texture, spectral information, Iterative Self-organizing Data Analysis Technique (ISODATA) results, Minimum Noise Fraction (MNF) results, and abundance) [35][36][37][38] Therefore, through decision tree algorithms, the potential ROI rules can be mined to establish classification tree for improving LULC analysis. Methodological framework for proposed improved decision tree method.…”

Section: Methodsmentioning

confidence: 99%

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Yang

Ding

et al. 2017

Remote Sensing

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Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

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Section: Methodsmentioning

confidence: 99%

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Yang

Ding

et al. 2017

Remote Sensing

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“…The most important block (and the one we specifically address in this paper) is the endmember extraction one, which provides prior information of pure materials for target detection [4], abundance mapping [5], change detection [6], and object classification [7]. As a result, proper extraction of pure endmembers is very important in hyperspectral data exploitation [8].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Convex Set Optimization for Spatial–Spectral Endmember Extraction From Hyperspectral Images

Shah

Zaveri

Trivedi

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spectral unmixing is an important problem for remotely sensed hyperspectral data exploitation. Automatic spectral unmixing can be viewed as a three-stage problem, where the first stage is subspace identification, the next one is endmember extraction, and the final one is abundance estimation. In this sequence, endmember extraction is the most challenging problem. Many researchers have attempted to extract endmembers from hyperspectral images using spectral information only. However, it is well known that the inclusion of spatial information can improve the endmember extraction task. In this paper, we introduce a new endmember extraction algorithm that exploits both spectral and spatial information. A main innovation of the proposed algorithm is that spatial information is exploited using entropy, while spectral information is exploited using convex set optimization. In the literature, none of the spatial-spectral algorithms has used entropy as spatial information. The inclusion of this entropy-based spatial information improves the accuracy of the endmember extraction process. The results obtained by the proposed algorithm are compared (using a variety of metrics) with those obtained by other state-of-the-art methods, using both synthetic and real datasets. Our experimental results demonstrate that the proposed algorithm outperforms many available algorithms.

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“…They are widespread in hyperspectral imagery and have become an obstacle for the high accuracy of ground target detection and classification. 3 Typical endmember extraction methods [3][4][5][6] include N-FINDR, 4 the simplex growing algorithm, 5 vertex component analysis (VCA), 6 and so on. 2 Among the various spectral unmixing methods, linear spectral unmixing has been the most widely used.…”

Section: Introductionmentioning

confidence: 99%

Endmember number estimation for hyperspectral imagery based on vertex component analysis

Liu

Zhang

2014

J. Appl. Remote Sens

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Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/termsAbstract. Endmember extraction is a crucial step in hyperspectral unmixing. For many endmember extraction algorithms, the number of endmembers is a precondition, and the accuracy of the endmember number directly affects the quality of the unmixing results. This paper proposes an automatic method for estimating the endmember number for hyperspectral imagery on the basis of vertex component analysis (VCA). The endmember extraction result of VCA is inconsistent because of the involvement of random vectors. This feature is utilized by our method to obtain the real endmember number. First, the endmember number is initialized with a small integer. Then, VCA is repeatedly implemented, and the endmember extraction results are different each time because of VCA's inconsistency. Finally, the real endmember number is determined from the union of these individual results. Extensive experiments were carried out on both simulated and real hyperspectral images, confirming the effectiveness of the proposed approach. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Fig. 4 AVIRIS Cuprite image scene. Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Liu, Du, and Zhang: Endmember number estimation for hyperspectral imagery. Downloaded Fro...

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Comparison of hyperspectral endmember extraction algorithms

Cited by 7 publications

References 11 publications

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Entropy-Based Convex Set Optimization for Spatial–Spectral Endmember Extraction From Hyperspectral Images

Endmember number estimation for hyperspectral imagery based on vertex component analysis

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