Laser & Optoelectronics Progress, Volume. 56, Issue 11, 112801(2019)
Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images
In this study, a deep adversarial domain adaptation method is proposed for cross-domain classification in high-resolution remote sensing images. A deep convolutional neural network VGG16 is used to learn the deep features of scene images. The adversarial learning method is used to minimize the difference of feature distribution between source and target domains. RSI-CB256(Remote Sensing Image Classification Benchmark), NWPU-RESISC45(Northwestern Polytechnical University Remote Sensing Image Scene Classification)and AID(Aerial Image data set) are used as source domain datasets, and UC-Merced(University of California, Merced)and WHU-RS 19(Wuhan University Remote Sensing)are used as target domain datasets. The experimental results denote that the proposed method can improve the generalization ability of the model for target domain dataset without labels.
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Wenxiu Teng, Ni Wang, Taisheng Chen, Benlin Wang, Menglin Chen, Huihui Shi. Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images[J]. Laser & Optoelectronics Progress, 2019, 56(11): 112801
Category: Remote Sensing and Sensors
Received: Dec. 17, 2018
Accepted: Dec. 25, 2018
Published Online: Jun. 13, 2019
The Author Email: Teng Wenxiu (wenxiu_teng@163.com), Wang Ni (wnstrive@163.com)