Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 19, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3068558
Keywords
Gallium nitride; Generators; Remote sensing; Generative adversarial networks; Feature extraction; Correlation; Vegetation mapping; Change detection; GAN; image-to-image translation; remote sensing
Categories
Funding
- National Key Research and Development Program of China [2018AAA0100602]
- National Natural Science Foundation of China [U1706218]
- Key Research and Development Program of Shandong Province [2019GHY112048]
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The existing remote sensing change detection methods are often affected by seasonal variations, leading to false detections. The proposed image translation method uses a style-based recalibration module and a new style discriminator to improve the detection performance significantly.
Existing remote sensing change detection methods are heavily affected by seasonal variation. Since vegetation colors are different between winter and summer, such variations are inclined to be falsely detected as changes. In this letter, we proposed an image translation method to solve the problem. A style-based recalibration module is introduced to capture seasonal features effectively. Then, a new style discriminator is designed to improve the translation performance. The discriminator can not only produce a decision for the fake or real sample but also return a style vector according to the channel-wise correlations. Extensive experiments are conducted on the season-varying data set. The experimental results show that the proposed method can effectively perform image translation, thereby consistently improving the season-varying image change detection performance. Our codes and data are available at https://github.com/summitgao/RSIT_SRM_ISD.
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