Integrating Hierarchical Segmentation Maps With MRF Prior for Classification of Hyperspectral Images in a Bayesian Framework

Abstract

This paper presents a new spectral-spatial classification method for hyperspectral (HS) images. The proposed method is based on integrating hierarchical segmentation results into Markov random field (MRF) spatial prior in the Bayesian framework. This work includes two main contributions. First, statistical region merging (SRM) segmentation algorithm is extended to a hierarchical version, HSRM. Second, a method for extracting a multilevel “fuzzy no-border/border” map from HSRM segmentation hierarchy is proposed, which are then exploited as weighting coefficients to modify the spatial prior of MRF-based multilevel logistic (MLL) model. The proposed method, named as MRF + HSRM, addresses the common problem of MRF-based methods, i.e., over-smoothing of classification result. Several experiments are conducted using real HS images to evaluate the performance of the proposed method in comparison with conventional MRF, and some state-of-the-art weighted MRF and object-based classifiers. To estimate the class conditional probability distribution in Bayesian framework, probabilistic support vector machine (SVM) and subspace multinomial logistic regression (MLRsub) classifiers are used. The experimental results demonstrate that the proposed method is able to generate more homogeneous regions similar to MRF-based methods, while preserving class boundaries as accurately as segmentation-based methods. The overhead computational burden of the proposed hierarchical segmentation stage is negligible considering the improvement it offers in classification results.