Evaluation on the Two Filling Functions for the Recovery of Forest Information in Mountainous Shadows on Landsat ETM + Image  

作　　者：SHAHTAHMASSEBI Amir reza MOORE Nathan 

机构地区：[1]Institute of Agriculture Remote Sensing and Information Technology,Zhejiang University [2]Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province [3]Department of Geography,Michigan State University

出　　处：《Journal of Mountain Science》2011年第3期414-426,共13页山地科学学报（英文）

基　　金：supported by the funding from National Natural Science Foundation of China(Grant No 30671212);partially by NASA projects NNX08AH50G and G05GD49G at Michigan State University

摘　　要：In general,topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images.In this paper,information in shadow was synthesized by using two filling techniques,namely,roifill and imfill,in order to improve the accuracy of forest mapping over mountainous regions.These two methods were applied to Landsat Enhanced Thematic Mapper (ETM +) multispectral image from Dong Yang County,Zhejiang Province,China.The performance of these methods was compared with two conventional techniques,including cosine correction and multisource classification.The results showed that by applying filling approaches,average overall accuracy of classification was improved by 14 percent.However,through conventional methods this value increased only by 9 percent.The results also revealed that estimated forest area on the basis of shadow-corrected images by 'roifill' technique was much closer to the survey data compared to traditional algorithms.Apart from this finding,our finding indicated that topographic shadow was an accentuated problem in medium resolution images such as Landsat ETM+ over mountainous regions.

关 键 词：SHADOW Imfill： Roifill： Landsat ETM＋ 

分 类 号：S771.8[农业科学—森林工程]