4.6 Article

A maximum entropy approach to one-class classification of remote sensing imagery

期刊

INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 31, 期 8, 页码 2227-2235

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161003702245

关键词

-

向作者/读者索取更多资源

In remote sensing classification there are situations when users are only interested in classifying one specific land type without considering other classes, which is referred to as one-class classification. Traditional supervised learning requires all classes that occur in the image to be exhaustively labelled and hence is inefficient for one-class classification. In this study we investigate a maximum entropy approach (MAXENT) to one-class classification of remote sensing imagery, i.e. classifying a single land class (e. g. urban areas, trees, grasses and soils) from an aerial photograph with 0.3 m spatial resolution. MAXENT estimates the Gibbs probability distribution that is proportional to the conditional probability of being positive. A threshold for generating binary predictions can be determined based on the omission rate of a validation set. The results indicate that MAXENT provides higher classification accuracy than the one-class support vector machine (OCSVM). MAXENT does not require other land classes for training. Its input is only a set of training samples of the specific land class of interest, as well as a set of known constraints on the distribution. Therefore, the effort of manually collecting training data for classification can be significantly reduced.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据