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：17-20
出版社：Copernicus Publications
摘要：This paper presents a new application of a data-clustering algorithm in Landsat image classification, which improves on conventional classification methods. Neural networks have been widely used in Landsat image classification because they are unbiased by data distribution. However, they need long training times for the network to get satisfactory classification accuracy. The data-clustering algorithm is based on fuzzy inferences using radial basis functions and clustering in input space. It only passes training data once so it has a short training time. It can also generate fuzzy classification, which is appropriate in the case of mixed, intermediate or complex cover pattern pixels. This algorithm is applied in the land cover classification of Landsat 7 ETM+ over the Rio Rancho area, New Mexico. It is compared with Back-Propagation Neural Network (BPNN) to illustrate its effectiveness and concluded that it can get a better classification using shorter training time
