Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing

Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability's influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of endmembers and fully constrained least squares based on distance strategy (D-FCLS) to improve the efficiency of abundance estimation. In the segmentation-scale determination method, this study establishes a segmentation scale division criterion to divide segmented images with similar quality into the same segmentation scale. The optimal segmentation scale is selected according to the actual situation of hyperspectral images. Moreover, the distance strategy is applied to fully constrained least squares (FCLS) using the spatial relationship between endmembers and the mixed pixel in abundance estimation. The proposed methods are evaluated on the synthetic and real datasets. The results show that the validity of the segmentation-scale determination method is verified by quantitative and qualitative evaluation on all datasets. In terms of abundance estimation, compared with FCLS, D-FCLS improves the efficiency by more than 10.30% on the synthetic dataset and 18.71% on the real dataset. In addition, this study's proposed abundance estimation method and unsupervised superpixel-based HU method are superior to the other comparison methods.