access icon free Sparsity measure based library aided unmixing of hyperspectral image

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.

Inspec keywords: learning (artificial intelligence); geophysical image processing; iterative methods; sparse matrices; image representation; image classification; hyperspectral imaging; remote sensing; feature extraction

Other keywords: sparsity measure; norm sparsity; remote sensing; pruned library; sparse inversion method; synthetic image datasets; sparse abundance matrix; parameter-free algorithm; library pruning; semiblind unmixing; highly coherent spectral library; abundance calculation task; computational efficiency; real image datasets; desirable sparsity properties; signal processing community; image endmembers; hyperspectral image; sparsity level; standard sparsity measures; library aided unmixing; application-specific spectral libraries; proficiency; abundance computation

Subjects: Linear algebra (numerical analysis); Knowledge engineering techniques; Geography and cartography computing; Other topics in statistics; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Image recognition; Other topics in Earth sciences; Other topics in statistics

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