Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery

Chang, Chein‐I; Chen, Shih‐Yu; Li, Hsiao-Chi; Chen, Hsian‐Min; Wen, Chia-Hsien

doi:10.1109/jstars.2016.2555960

Cited by 49 publications

(25 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this regard, automated endmember bundles (AEB) [19] has established endmembers sets by executing standard endmember extraction algorithms such as N-FINDR [20], orthogonal subspace projection (OSP) [21], unsupervised fully constrained least squares (UFCLS) [22], iterative error analysis (IEA) [23] and vertex component analysis (VCA) [24] and clustering the resulted endmembers from different methods. Recently, the authors in [25] showed that VCA is essentially the same as simplex growing algorithm (SGA) [26] as long as their initial conditions are the same. So, other conventional endmember extraction algorithms such as SGA can be used in AEB.…”

Section: Establishing a Set Of Spectral Variabilities For Each Endmembermentioning

confidence: 99%

Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands

2017

View full text Add to dashboard Cite

Abstract:Variable environmental conditions cause different spectral responses of scene endmembers. Ignoring these variations affects the accuracy of fractional abundances obtained from linear spectral unmixing. On the other hand, the correlation between the bands of hyperspectral data is not considered by conventional methods developed for dealing with spectral variability. In this paper, a novel approach is proposed to simultaneously mitigate spectral variability and reduce correlation among different endmembers in hyperspectral datasets. The idea of the proposed method is to utilize the angular discrepancy of bands in the Prototype Space (PS), which is constructed using the endmembers of the image. Using the concepts of PS, in which each band is treated as a space point, we proposed a method to identify independent bands according to their angles. The proposed method comprised two main steps. In the first step, which aims to alleviate the spectral variability issue, image bands are prioritized based on their standard deviations computed over some sets of endmembers. Independent bands are then recognized in the prototype space, employing the angles between the prioritized bands. Finally, the unmixing process is done using the selected bands. In addition, the paper presents a technique to form a spectral library of endmembers' variability (sets of endmembers). The proposed method extracts endmembers sets directly from the image data via a modified version of unsupervised spatial-spectral preprocessing. The performance of the proposed method was evaluated by five simulated images and three real hyperspectral datasets. The experiments show that the proposed method-using both groups of spectral variability reduction methods and independent band selection methods-produces better results compared to the conventional methods of each group. The improvement in the performance of the proposed method is observed in terms of more appropriate bands being selected and more accurate fractional abundance values being estimated.

show abstract

Section: Establishing a Set Of Spectral Variabilities For Each Endmembermentioning

confidence: 99%

Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands

2017

View full text Add to dashboard Cite

show abstract

“…Here we use three different algorithms for extracting endmembers from hyperspectral data. The first one is Automatic Target Generation Process (ATGP) that finds its targets by using a sequence of orthogonal subspaces with the maximal orthogonal projections [2], [5], [7], [8] where ATGP considered the unsupervised version of Orthogonal Subspace Projection (OSP) algorithm. The second used algorithm is the Simplex Growing Algorithm (SGA) [3], [8] which finds its endmembers by growing a simplex, vertex by vertex, until it reaches the required endmembers represented by vertices of simplex.…”

Section: Introduction Imentioning

confidence: 99%

“…The first one is Automatic Target Generation Process (ATGP) that finds its targets by using a sequence of orthogonal subspaces with the maximal orthogonal projections [2], [5], [7], [8] where ATGP considered the unsupervised version of Orthogonal Subspace Projection (OSP) algorithm. The second used algorithm is the Simplex Growing Algorithm (SGA) [3], [8] which finds its endmembers by growing a simplex, vertex by vertex, until it reaches the required endmembers represented by vertices of simplex. The last used algorithm is the Vertex Component Analysis (VCA) [4], [8], it is an OP-based EEA that is characterized by computational complexity reduction by replacing simple volume calculation with OP and growing nonnegative convex hulls, vertex by vertex, until it builds a pvertex convex hull (p denotes the endmembers required to be extracted).…”

Section: Introduction Imentioning

confidence: 99%

“…The second used algorithm is the Simplex Growing Algorithm (SGA) [3], [8] which finds its endmembers by growing a simplex, vertex by vertex, until it reaches the required endmembers represented by vertices of simplex. The last used algorithm is the Vertex Component Analysis (VCA) [4], [8], it is an OP-based EEA that is characterized by computational complexity reduction by replacing simple volume calculation with OP and growing nonnegative convex hulls, vertex by vertex, until it builds a pvertex convex hull (p denotes the endmembers required to be extracted).…”

Section: Introduction Imentioning

confidence: 99%

“…ATGP, VCA, SGA are most widely used in EE [8]. They are similar in their design but different in preprocessing steps.…”

Section: Introduction Imentioning

confidence: 99%

See 2 more Smart Citations

New Divide and Conquer Method on Endmember Extraction Techniques

Samir¹,

Abdellatif²,

Badr³

2017

ijacsa

View full text Add to dashboard Cite

Abstract-In hyperspectral imagery, endmember extraction (EE) is a main stage in hyperspectral unmixing process where its role lies in extracting distinct spectral signature, endmembers, from hyperspectral image which is considered as the main input for unsupervised hyperspectral unmixing to generate the abundance fractions for every pixel in hyperspectral data. EE process has some difficulties. There are less distinct endmembers than its mixed background; also, there are endmembers that have rare occurrences in data that are considered as difficulties in EE process. In this paper, we propose a new technique that uses divide and conquer method for EE process to find out these difficult (rare or less distinct) endmembers. divide and conquer method is used to divide hyperspectral data scene to multiple divisions and take each division as a standalone scene to enable endmember extraction algorithms (EEAs) to extract difficult endmembers easily and finally conquer all extracted endmembers from all divisions. We implemented this method on real dataset using three EEAs: ATGP, VCA, and SGA and recorded the results that outperform the results from usual endmember extraction techniques methods in all used algorithms.

show abstract

Introduction

Chang

2022

Advances in Hyperspectral Image Processing Techniques

View full text Add to dashboard Cite

Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery

Cited by 49 publications

References 29 publications

Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands

Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands

New Divide and Conquer Method on Endmember Extraction Techniques

Introduction

Contact Info

Product

Resources

About