Abstract
Sparse unmixing with a semisupervised fashion has been applied to hyperspectral remote sensing imagery. However, the imprecise spatial contextual information, the lack of global feature and the high mutual coherences of a spectral library greatly limit the performance of sparse unmixing. In order to address these prominent problems, a new paradigm to characterize sparse hyperspectral unmixing is proposed, namely, the superpixel-based weighted collaborative sparse regression and reweighted low-rank representation unmixing (SBWCRLRU). In this method, the weighted collaborative sparse regression explores the pixels shared the same support set to help the sparsity of abundance fraction, and the reweighted low rank representation minimizes the rank of the abundance matrix to promote the spatial consistency of the image. Meanwhile, superpixel segmentation is adopted to cluster the pixels into different spatial homogeneous regions to further improve the unmixing performance. Extensive experiments results conducted on both synthetic and real data demonstrate the effectiveness of the proposed SBWCRLRU. It can not only improve the performance of hyperspectral unmixing but also outperform the existing sparse unmixing approaches.
Highlights
Hyperspectral imagery (HSI) has high spectral resolution since it can capture hundreds of narrow and adjacent spectra in the scene simultaneously
The linear mixture model (LMM) assumes that the mixing between objects occurs on a macroscopic scale and that the incident solar radiation only interacts with one material
A new approach of sparse spectral unmixing algorithm based on superpixel weighted collaborative sparse regression and reweighted low rank representation for hyperspectral image unmixing (SBWCRLRU) is proposed, and both spectral correlative and spatial low-rank characteristics of each superpixel in HSI are considered in this method
Summary
Hyperspectral imagery (HSI) has high spectral resolution (usually less than the order of nm) since it can capture hundreds of narrow and adjacent spectra in the scene simultaneously. In [21]–[23], the strategies of iteratively weighted sparsity obtained satisfactory results These algorithms only interpret the sparsity of abundance coefficients and the availability of spectral information, but spatial contextual information is generally ignored, which may provide limited unmixing performance and high computational complexity, especially for observed HSI degraded by noise. A fast sparse multiscale sparse unmixing algorithm (MUA) is proposed [30], where spectral unmixing is first performed in the approximate domain images by using superpixel segmentation method, and the coarse domain unmixing results are used to guide the original domain unmixing to obtain more meaningful spatial information. A new approach of sparse spectral unmixing algorithm based on superpixel weighted collaborative sparse regression and reweighted low rank representation for hyperspectral image unmixing (SBWCRLRU) is proposed, and both spectral correlative and spatial low-rank characteristics of each superpixel in HSI are considered in this method.
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