文献类型：期刊文献

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

作者：Chen, Chenxin[1] Tang, Ping[1] Wu, Honggan[2]

第一作者：Chen, Chenxin

通信作者：Tang, P[1]

机构：[1]Chinese Acad Sci, Inst Remote Sensing Applicat, Beijing, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China

年份：2013

卷号：34

期号：23

起止页码：8518-8533

外文期刊名：INTERNATIONAL JOURNAL OF REMOTE SENSING

收录：;EI(收录号：20134316904536);Scopus(收录号：2-s2.0-84886895206);WOS:【SCI-EXPANDED(收录号:WOS:000325515500016)】；

基金：This research was supported by the High Technology Research and Development Project (project no. 2009AA122002) of China. The authors would like to thank the China Remote Sensing Satellite Ground Station for providing the TM images and the Chinese Academy of Forestry for providing other data sets. We would also like to thank the two anonymous reviewers whose comments significantly improved this article.

语种：英文

外文关键词：Classification (of information) - Data reduction - Gaussian distribution - Land use - Maximum likelihood - Remote sensing - Surveying

摘要：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.