期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 11, 期 1, 页码 259-263出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2013.2255258
关键词
Active learning; classification; large-scale land cover; MODIS sensor; support vector machines (SVMs); transfer learning
In this letter, we show how active learning can be particularly promising for classifying remote sensing images at large scales. The classification model constructed on samples extracted from a limited region of the image, called source domain, exhibits generally poor accuracies when used to predict the samples of a different region, called target domain, due to possible changes in class distributions throughout the image. To alleviate this problem, we suggest selecting and labeling additional samples from the new domain in order to improve generalization capabilities of the model. We propose to implement an initialization strategy based on clustering before applying the traditional active learning method in order to cope with distribution changes and better explore the feature space of the target domain. Experiments on a MODIS dataset for the generation of a land-cover map at European scale show good capabilities of the proposed approach for this purpose.
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。
推荐
暂无数据