Scene classification using combined spectral, textural, and contextual information

Abstract

A classification scheme incorporating spectral, textural, and contextual information is detailed in this paper. The gray level co-occurrence matrix (GLCM) is calculated to generate texture features. Those features are then subjected to a selection process for joining with spectral data in order to evaluate their discrimination capability in classification performance. The classification result is further enhanced with contexture in terms of a refined Markov random field (MRF) model. Multiscale edge features are derived to overcome the bias generally contributed by the presence of edge pixels during the MRF classification process. The smooth weighting parameter for the refined MRF model is chosen based on the probability histogram analysis of those edge pixels. The maximum a posterior margin (MPM) algorithm is used to search the solution. The joining of texture with spectral data produces a significant enhancement in classification accuracy. The refined MRF-model with a soft version line process, in comparison with the traditional MRF model, successfully restricted the commonly found over-smoothed result, and simultaneously improved the classification accuracy and visual interpretation.