Robust spectral clustering integrated nonlocal information blocks for hyperspectral image classification

Abstract

Hyperspectral image (HSI) clustering is a challenging task due to its containing rich spectral information and spatial features. Subspace clustering has been used to explore the intrinsic connections between data points successfully. Graph convolutional subspace clustering (GCSC) was used to achieve robust HSI clustering. The model remaps the self-expressions of the data to non-Euclidean domains to generate a robust graph embedding dictionary. The efficient kernel graph convolutional subspace clustering model utilizes a subspace clustering model with the Frobenius norm and a Gaussian kernel function to achieve a globally optimal closed-form solution that is easy to implement, train, and apply. To reduce the impact of image noise on the segmentation effect, we consider spatial nonlocal self-similarity using nonlocal information blocks based on the fact that the spectral features of HSIs lie in low-dimensional subspaces. At the same time, the three-dimensional tensor generated by the nonlocal similar image blocks is estimated using a robust statistic function. Principal component analysis is used to remove the redundant information of hyperspectral data and extract the main features of pixels. Then a robust statistical function and nonlocal information block are used to construct the affinity matrix. The redundant information prevalent in the whole natural image is used for denoising, that is, the image block is taken as the unit to find similar areas in the image, then these regions are weighted and averaged. Since all pixels in the image are used, the noise can be better filtered. Finally, the constructed affinity matrix is applied to spectral clustering to obtain better clustering results. The model used is called nonlocal mean robust statistical kernel graph convolutional subspace clustering (N-RKGCSC). Experimental results on Salinas, Indian Pines, Pavia Center, and Pavia University datasets showed that the N-RKGCSC model could eliminate the interference of additive and multiplicative noise well, achieving better segmentation performance and better noise immunity.