Markov Random Fields Integrating Adaptive Interclass-Pair Penalty and Spectral Similarity for Hyperspectral Image Classification

摘要

This paper presents a novel Markov random field (MRF) method integrating adaptive interclass-pair penalty (aICP(2)) and spectral similarity information (SSI) for hyperspectral image (HSI) classification. aICP(2) structurally combines K(K - 1)/2 (K is the number of classes) classical "Potts model" with K(K - 1)/2 interaction coefficients. aICP(2) tries a new way to solve the key problems, insufficient correction within homogeneous regions, and over-smoothness at class boundaries, in MRF-based HSI classification. It is assumed that different class pairs should be assigned with various degrees of penalties in MRF smoothness process, according to pairwise class separability and spatial class confusion in raw classification map. The Fisher ratio is modified to measure pairwise class separability with a training set. And, gray level co-occurrence matrix is used to measure spatial class confusion degree. Then, aICP(2) is constructed by combining Fisher ratio and GCLM. aICP(2) applies larger penalty on class pairs that confuse with each other seriously to provide sufficient smoothness, and vice versa. In addition, to protect class edges and details, SSI is introduced to make the penalty of related neighboring pixels small. aICP(2)ssi denotes the integration of aICP(2) and SSI. The further improved method is both interclass-pair and interpixel adaptive in spatial term. A graph-cut-based alpha-beta-swap method is introduced to optimize the proposed energy function. The experimental results on real HSI data indicate that the proposed method outperforms compared MRF-based and other spectral-spatial approaches in terms of classification accuracies and region uniformity.