Sparse Unmixing of Hyperspectral Images With Noise Reduction Using Spatial Filtering

This is the authors' abstract. We don't add key points for this paper.

Sparse unmixing has emerged as a powerful technique for addressing the presence of mixed pixels in hyperspectral images. A commonly employed approach involves integrating spectral information and spatial features within a sparse unmixing framework, with the ultimate goal of improving interpretability for remote sensing applications. In practical situations, hyperspectral images frequently encounter various forms of noise contamination, posing challenges to the differentiation of target signals from mixed noise and complicating the interpretation of mixed pixels. To tackle this issue, we introduce a new spatial filtering-based sparse unmixing (SFSU) algorithm. The proposed SFSU model combines filter-guided dual spatial weighting factors and a regularization term to minimize abundance errors within the unmixing framework. By means of this integration, the model facilitates the analysis of spatial information in the image from multiple perspectives, with the objective of mitigating the impact of different noise types on the unmixing outcomes. In the first part of the SFSU model, the original spatial information of the hyperspectral image is preserved by utilizing a precomputed filter-guided spatial weighting factor, thereby augmenting the sparsity of the abundance estimation process. Subsequently, a local neighborhood spatial weighting factor is employed to achieve segmented smoothing in the abundance maps. In the regularization term for reducing abundance errors, multiple filters are introduced to improve the algorithm’s robustness to various types of noise by using the difference between the filtered and denoised abundance map and the expected abundance map as a constraint. Extensive experiments conducted on both simulated and real hyperspectral datasets demonstrate the exceptional performance of the SFSU algorithm in effectively addressing complex mixed noise pollution. Moreover, the proposed spatial filtering strategy has been proven to significantly improve the accuracy of unmixing.