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：2008
卷号：XXXVII Part B7
页码：589-594
出版社：Copernicus Publications
摘要：This paper introduces a new approach, nearest convex hull (NCH), for remote sensing classification. NCH is an intuitive classification method which labels the test point as the training class whose convex hull is closest to it. Some attractive advantages of this learning algorithm are the robustness to noises and the scale of training samples, the straightforward way to handle multi- class tasks, and most of all the capability of processing high dimensional and nonlinear data. In our work, we deduce the NCH algorithm again basing on theories of the computational geometry, from which a simpler implementation of it is presented. Then we apply it to real-world remote problems and compare it with two other state-of-arts classifiers: K-NN and SVM. Experiments in this paper confirm the promising performance of NCH for remote sensing classification
关键词：Pattern recognition; Classification; Image understanding; Accuracy analysis; Land cover
