Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach  

作　　者：LI Huapeng ZHANG Shuqing SUN Yan GAO Jing 

机构地区：[1]Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China [2]Graduate University of Chinese Academy of Sciences, Beijing 100049, China [3]School of Computer and Information Science, University of South Australia, Adelaide 5095, Australia

出　　处：《Chinese Geographical Science》2011年第3期312-321,共10页中国地理科学（英文版）

基　　金：Under the auspices of National Natural Science Foundation of China (No.40871188);Knowledge Innovation Programs of Chinese Academy of Sciences (No.INFO-115-C01-SDB4-05)

摘　　要：Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning （ER） approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier （MLC）.In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources （TM plus altitude and slope） are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

关 键 词：evidential reasoning Dempster-Shafer theory of evidence multi-source data geographic ancillary data land cover classification classification uncertainty 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] Q948.15[自动化与计算机技术—控制科学与工程]