Low-Rank Subspace Unmixing of Remotely Sensed Hyperspectral Image

Spectral unmixing is an important technique for hyperspectral image application, which aims to estimate the pure spectral signatures in each mixed pixel and their corresponding fractional abundances. However, due to the influence of factors such as illumination, topography change and atmosphere, spectral variability is inevitable, which will lead to inaccurate unmixing results. Traditional unmixing methods fail to handle this problem, especially the complex spectral variability in the image. To address this limitation, a new technique called low-rank subspace unmixing (LRSU) was established, which aims to jointly estimate a subspace projection and abundance maps. For the proposed LRSU approach, the original data is projected into a low-rank subspace to deal with various spectral variabilities in spectral unmixing. Meanwhile, the spectral-spatial weighted sparse regularization term is introduced to upgrade the sparsity of the solution and capture the piecewise smooth structure of the data. The experimental results, conducted using synthetic data sets, quantitatively indicate that the proposed LRSU strategy produces better results than other advanced spectral unmixing methods.

1. Introduction

Due to the limitation of the spatial resolution of imaging spectrometer, mixed pixels are abundant in hyperspectral images [1]. The existence of mixed pixels interferes with the research and analysis of hyperspectral images [2]. In order to solve this problem, spectral unmixing has been used to identify the pure components (endmembers) in a scene and to estimate the abundance maps of such pure signatures in each mixed pixel [3], [4].