Improving Spectral-Based Endmember Finding by Exploring Spatial Context for Hyperspectral Unmixing

• Main Authors: Shaohui Mei, Ge Zhang, Jun Li, Yifan Zhang, Qian Du
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Endmember extraction; hyperspectral unmixing; singular value decomposition; sparse representation; spatial preprocessing; spatial postprocessing
• Online Access: https://ieeexplore.ieee.org/document/9120337/

Hyperspectral unmixing, which intends to decompose mixed pixels into a collection of endmembers weighted by their corresponding fraction abundances, has been widely utilized for remote sensing image exploitation. Recent studies have revealed that spatial context of pixels is important complemental information for hyperspectral image processing. However, many well-known endmember finding (EF) algorithms identify spectrally pure spectra from hyperspectral images according to spectral information only, resulting in limited accuracy of hyperspectral unmixing application since they ignore spatial distribution or structure information in the image. Therefore, in this article, several novel spatial exploiting (SE) strategies are proposed to improve the performance of the well-known spectral-based EF (sEF) algorithms by integrating spatial information. Three different spatial exploiting strategies are designed to use pixel spatial context, by which the spectral variation of pixels can be alleviated to improve the performance of hyperspectral unmixing. Specifically, in pixel domain, the pixels are linearly reconstructed using their neighbors in which the spatially derived factor to weight the importance of the spectral information is generated using local linear representation and local sparse representation, while in the feature domain, pixels are revised using dominated features of neighboring pixels in singular value decomposition. The proposed spatial exploiting strategies can not only be used as a preprocessing stage to revise pixels for sEF algorithms, but also be used as a postprocessing step to revise endmembers found via sEF algorithms. Finally, experimental results on both synthetic and real hyperspectral datasets demonstrate that the proposed SE strategies can certainly improve the performance of several well-known sEF algorithms, and obtain more accurate unmixing results than several state-of-the-art spatial preprocessing methods.