Improving classification of woodland types using modified prior probabilities and Gaussian mixed model in mountainous landscapes

Abstract

A modified maximum-likelihood (ML) classifier was applied to increase the accuracy of land-cover classification over a complex mountain landscape. The traditional ML classifier is a robust parametric approach in remote-sensing image classification. However, it is difficult to improve classification accuracy when using the traditional ML classifier in complex landscapes such as mountainous regions. In this study, we demonstrated a modified ML classifier that uses the non-equal prior probabilities derived from digital elevation model (DEM) ancillary data and a Gaussian mixed model (GMM) to delineate land-cover types within forest stands. We designed and compared four experiments using Landsat Thematic Mapper (TM) images covering the Culai Hill region of the eastern territory of China: (1) traditional ML classification with equal prior probability, (2) modified ML classification with non-equal prior probability derived from elevation information, (3) Gaussian mixed classifier (GMC) with equal prior probability, and (4) GMC with non-equal prior probability. Overall, the highest accuracy (80.5%) was obtained using the GMC with variable prior probabilities. The GMC with equal prior probabilities and the ML using non-equal prior probabilities yielded maps with accuracy of 74.7% and 78.0%, respectively, values significantly higher than that obtained using the conventional ML method. This implies that use of modified prior probabilities and GMM analysis has considerable potential to increase the accuracy of land-use and land-cover classification using TM imagery for complex landscapes such as the Culai Hill region.