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- W2571179428 abstract "In this paper, we propose a novel joint classification framework for multi-source image change detection, the multi-source image-pair is generated by different sensors, such as optical sensor and synthetic aperture radar, respectively. This framework is established for feature learning, which is based on deep neural networks. Firstly, in order to segment the optical image, deep neural networks are essential to extract deep features for clustering segmentation. Then the stacked denoising autoencoders is trained to learn capability of classification via choosing part of reliable segmentation results of optical image as labels. Next, the other image of the image-pair is entered in the trained stacked denoising autoencoders to classification automatically. Afterwards, two images passed joint classification are obtained. Finally, the difference image is produced by comparing the two images passed joint classification. Experimental results illustrate that the method can be applied to multi-source image and outperforms the state-of-the-art methods." @default.
- W2571179428 created "2017-01-13" @default.
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- W2571179428 date "2016-01-01" @default.
- W2571179428 modified "2023-09-26" @default.
- W2571179428 title "DNN-Based Joint Classification for Multi-source Image Change Detection" @default.
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- W2571179428 doi "https://doi.org/10.1007/978-981-10-3611-8_37" @default.
- W2571179428 hasPublicationYear "2016" @default.
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