First, to acquire the feature distribution of a class, a histogram of each class (HOC) based on the raw classification map is generated. The major steps of the proposed algorithm are given as follows. To solve this problem, this study integrates histogram distribution analysis, double-window flexible pace search (DFPS), and box-whisker plot (BP) techniques into an iterative algorithm to enrich training samples. However, labeling samples is often a labor-intensive and time-consuming task. Training samples are usually required to train a classifier for supervised classification of very high spatial resolution (VHR) remote sensing images. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration's Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments.

The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior.

We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously.

In this paper, we propose a new framework for spectral-spatial classification of hyperspectral image data.

Loopy sanchez