Robust Spatial–Spectral Block-Diagonal Structure Representation With Fuzzy Class Probability for Hyperspectral Image Classification

Abstract

Generally, the apparent spectral information of hyperspectral images (HSIs) is directly used to measure the similarity among HSI pixels in the feature space, but this process cannot reveal the inherent characteristics of HSI pixels. Moreover, constructing spatial–spectral block-diagonal subspace structure representations of intraclass land-cover samples remains a challenge for low-rank representation (LRR) in HSI classification. In this article, we propose two methods to reveal the complex intrinsic spatial–spectral features of HSIs using block-diagonal subspace structures, namely, the spatial–spectral block-diagonal structure representation with class probability (SSBDCP) and spatial-spectral block-diagonal structure representation with fuzzy class probability (SSBDFCP) methods, for HSI classification. First, the SSBDFCP and SSBDCP methods explore the structure similarity characteristics of the latent subspace to form a block-diagonal LRR (BDLRR) of intraclass pixels with class probability and fuzzy class probability (FCP) and suppress the interclass pixels’ representations. Then, the spatial information is considered in the proposed methods to enhance spatial–spectral graph expression and capture more comprehensive information. Note that SSBDFCP can perform better than SSBDCP because the FCP considers the “typicalness” that a sample belongs to a specific category and utilizes complex intrinsic discriminative information based on the feedback of “weakly” supervised information. Moreover, the feedback information can remove the noise features around the pixels and take advantage of the benefits of true neighbors. The experimental results for the Indian Pines and Pavia University data sets show that the SSBDFCP and SSBDCP methods achieve better HSI classification results than other popular graph construction methods.