期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B7
页码：927-932
出版社：Copernicus Publications
摘要：A set of ERS SAR and optical MODIS-images were classified to land cover and tree species classes. Different methods for pixel and decision based data fusion were tested. Classifications of featuresets were carried out using Bayes rule for minimum error. The results were not very successful, the classification accuracies of land cover classes varied from 43% to 75%, depending on the used features and classes. The decision based data f usion method, where the a'posteriori probabilities representing the proportions of different land cover classes of low resolution classification are used as a'prior probabilities in high resolution classif ication looks promising. Using this method, the increase of overall and classwise accuracies can be more than 10 and 25 %-units, respectively
关键词：Forestry; Land Cover; Classification; Fusion; Optical; SAR; Multitemporal
