Multiscale Spatial Sparse Unmixing for Remotely Sensed Hyperspectral Imagery

Abstract

Spectral unmixing is a crucial aspect of hyperspectral image processing. Given the low spatial resolution of hyperspectral remote sensing sensors, combined with the complexity and diversity of actual ground objects, hyperspectral remote sensing images often contain numerous mixed pixels, which make spectral unmixing a challenging task. Recent advancements in spectral libraries have shown promising results for decomposing mixed pixels in hyperspectral remote sensing images. Sparse unmixing, a semi-supervised unmixing strategy, avoids the drawbacks of blind source unmixing algorithms, which may extract virtual endmembers with no physical meaning. In this paper, we propose the multiscale spatial sparse unmixing (MSSU) algorithm, which utilizes the signal adaptive spatial multiscale unmixing of the over-segmentation method to decompose the complex unmixing problem. Furthermore, weighting factors are introduced to extract spatial information from the spectral image. The experimental results obtained from simulated hyperspectral datasets reveal the great potential of the proposed algorithm in unmixing.