期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 16, 期 1, 页码 45-49出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2868704
关键词
Change detection; conditional generative adversarial networks (cGANs); heterogeneous images; synthetic aperture radar (SAR)
类别
资金
- National Natural Science Foundation of China [61772393]
- National Program for Support of Top-Notch Young Professionals of China
- National Key Research and Development Program of China [2017YFB0802200]
- Key Research and Development Program of Shaanxi Province [2018ZDXM-GY-045]
Due to the distinct statistical properties in cross-sensor images, change detection in heterogeneous images is much more challenging than in homogeneous images. In this letter, we adopt a conditional generative adversarial network (cGAN) to transform the heterogeneous synthetic aperture radar (SAR) and optical images into some space where their information has a more consistent representation, making the direct comparison feasible. Our proposed framework contains a cGAN-based translation network that aims to translate the optical image with the SAR image as a target, and an approximation network that approximates the SAR image to the translated one by reducing their pixelwise difference. The two networks are updated alternately and when they are both trained well, the two translated and approximated images can be considered as homogeneous, from which the final change map can be acquired by direct comparison. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed framework.
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