期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 16, 期 7, 页码 1155-1159出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2889967
关键词
Domain adaptation; hyperspectral image classification; manifold embedding; maximum mean discrepancy (MMD); neural network; remote sensing
类别
资金
- National Natural Science Foundation of China [41431175, 61822113, 41871243, 61671335]
Hyperspectral remote sensing image classification has drawn a great attention in recent years due to the development of remote sensing technology. To build a high confident classifier, the large number of labeled data is very important, e.g., the success of deep learning technique. Indeed, the acquisition of labeled data is usually very expensive, especially for the remote sensing images, which usually needs to survey outside. To address this problem, in this letter, we propose a domain adaptation method by learning the manifold embedding and matching the discriminative distribution in source domain with neural networks for hyperspectral image classification. Specifically, we use the discriminative information of source image to train the classifier for the source and target images. To make the classifier can work well on both domains, we minimize the distribution shift between the two domains in an embedding space with prior class distribution in the source domain. Meanwhile, to avoid the distortion mapping of the target domain in the embedding space, we try to keep the manifold relation of the samples in the embedding space. Then, we learn the embedding on source domain and target domain by minimizing the three criteria simultaneously based on a neural network. The experimental results on two hyperspectral remote sensing images have shown that our proposed method can outperform several baseline methods.
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