期刊
REMOTE SENSING OF ENVIRONMENT
卷 115, 期 9, 页码 2232-2242出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2011.04.022
关键词
Active learning; Covariate shift; VHR; Hyperspectral; Remote sensing; Image classification
资金
- Swiss National Science Foundation [PBLAP2-127713/1]
The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and clustering of the data space are considered to perform active selection. Experiments on urban and agricultural images show the great potential of the proposed strategy to perform model adaptation. (C) 2011 Elsevier Inc. All rights reserved.
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