Noise robust estimation of number of endmembers in a hyperspectral image by Eigenvalue based gap index

Das, Samiran; Routray, Aurobinda; Deb, Alok Kanti

doi:10.1109/whispers.2016.8071676

Cited by 10 publications

(5 citation statements)

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“…As per random matrix theory, this task is equivalent to the numerical rank estimation of a matrix. The performance of the prevalent approaches, such as Harsanyi Ferrand Chang-virtual dimensionality, 58 hyperspectral subspace estimation by minimum error, 59 eigenvalue thresholding, 60 eigenvalue index, 61 nearest-neighbor distance ratio approach, 62 and Bioucas-Dias et al, 59 Das et al 60 gets compromised in the presence of noise and the sample size. Typically, the initial eigenvalues in the plot exhibit a sharp decrease, followed by the subsequent flat region, which corresponds to non-signal components.…”

Section: Estimation Of the Number Of Endmembersmentioning

confidence: 99%

Unmixing aware compression of hyperspectral image by rank aware orthogonal parallel factorization decomposition

Das,

Ghosal

2023

J. Appl. Rem. Sens.

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.Efficient compression is pertinent for the convenient storage, transmission, and processing of modern high-resolution hyperspectral images (HSI). We propose a high-performance HSI compression method using library-based spectral unmixing and tensor decomposition. Unlike the existing approaches, our proposed work incorporates unmixing in the compression framework and achieves significantly higher compression performance with negligible loss. The proposed library-based unmixing method includes an index for accurate endmember number estimation, followed by exact library pruning and a sparsity regularized formulation with norm-smoothing to compute the abundance maps. As the spectral library is available at the reconstruction (decoder) side also; compressing the abundance maps is as good as compressing the original HSI data. Since the abundance constraints used for the unmixing indicate the correlation of the abundance maps, compressing all abundance maps seems to cause redundant computation. A metric using the image smoothness and information measures is used here to identify the abundance map hardest to compress and the remaining part is left uncompressed. Subsequently, the work compresses the abundance map tensor using orthogonal PARAFAC decomposition with optimal rank determination. The orthogonalization process ensures that the factors span independent subspaces and reduces redundancy, whereas the rank selection prevents noisy or insignificant components. Extensive experiments are carried out to demonstrate that the unmixing workflow leads to negligible loss due to accurate endmember number estimation, exact library pruning, and accurate physically meaningful sparse inversion. Comparative assessments of compression efficacy suggest that the proposed work corresponds to better compression performance and higher classification accuracy.

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Section: Estimation Of the Number Of Endmembersmentioning

confidence: 99%

Unmixing aware compression of hyperspectral image by rank aware orthogonal parallel factorization decomposition

Das,

Ghosal

2023

J. Appl. Rem. Sens.

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“…The eigenvalues are arranged in descending order with a longtailed pattern, and he higher eigenvalues correspond to signal content, and the lower eigenvalues represent noise components or insignificant sources (32). However, instead of finding a hard threshold for separation of signal components from noisy components (9), we propose an alternate index, termed GAP index (33) which is defined by-…”

Section: B Dictionary Pruning By Gap Index Based Virtual Dimensionalmentioning

confidence: 99%

“…As the GAP Index computation step includes the standard deviation of the eigenvalues, as a consequence perturbation of eigenvalues due to noise affects both the numerator and denominator simultaneously. We had presented the concept in a conference work (33). Next, we utilized the GAP index-based estimator for dictionary pruning by using a two-step approach.…”

Section: B Dictionary Pruning By Gap Index Based Virtual Dimensionalmentioning

confidence: 99%

Semi-blind Unmixing of Hyperspectral Image by Eigenvalue based Virtual Dimensionality Estimation

Das¹

2020

Preprint

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“…Virtual dimensionality or number of endmembers in an image is defined as the number of spectrally distinct materials present in the image [29]. Prevalent methods include Harsanyi Ferrand Chang Virtual Dimensionality (HFC‐VD) [30], Hyperspectral subspace estimation (HySIME) [29], empirical automatic estimation (ELM) [31], eigenvalue thresholding [32], eigengap (GAP‐VD) [33] entropy estimation of eigenvalue [34], low‐rank subspace representation [35], Akaike information criteria (AIC) [36], minimum description length (MDL) [37], maximal orthogonal complement algorithm (MOCA) [38], high‐order statistics (HOS)‐HFC [39], HySURE [40], GENE‐CH [41], GENE‐AH [41] and so on. We have used GAP‐VD [33] scheme for VD‐estimation due to its superior performance in unfavourable scenarios and low computational runtime requirement.…”

Section: Our Proposed Semi‐blind Unmixing Using Sparsity Measuresmentioning

confidence: 99%

Sparsity measure based library aided unmixing of hyperspectral image

2019

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Availability of a large number of application‐specific spectral libraries has generated a great deal of interest in semi‐blind unmixing of the hyperspectral image in both remote sensing and signal processing community. This study presents a novel, semi‐supervised, parameter‐free algorithm which employs sparsity measures for library pruning. The overall algorithm includes sparsity criteria based library pruning and sparse inversion method for abundance computation. In the pruning process, each library element is removed from the spectral library and the corresponding sparse abundance matrix is computed. The library elements which lead to higher sparsity are adjudged as image endmembers, based on the assumption that elimination of actual image endmember enhances sparsity level. The authors also present a detailed exploration of standard sparsity measures. They calculate the abundance of the pruned library by maximising Gini index or pq‐norm sparsity, which satisfies the desirable sparsity properties and is easier to compute. The abundance calculation task is solved using the adaptive direction method of multipliers. The experimental results on several real and synthetic image datasets demonstrate the computational efficiency and proficiency the authors’ method in the presence of noise and highly coherent spectral library.

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Noise robust estimation of number of endmembers in a hyperspectral image by Eigenvalue based gap index

Cited by 10 publications

References 10 publications

Unmixing aware compression of hyperspectral image by rank aware orthogonal parallel factorization decomposition

Unmixing aware compression of hyperspectral image by rank aware orthogonal parallel factorization decomposition

Semi-blind Unmixing of Hyperspectral Image by Eigenvalue based Virtual Dimensionality Estimation

Sparsity measure based library aided unmixing of hyperspectral image

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