Remote Sensing Image Translation via Style-Based Recalibration Module and Improved Style Discriminator

Autor:	Zhang, Tiange, Gao, Feng, Dong, Junyu, Du, Qian
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
DOI:	10.1109/LGRS.2021.3068558
Popis:	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 season-varying dataset. 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. Comment: Accepted by IEEE Geoscience and Remote Sensing Letters, Code: https://github.com/summitgao/RSIT_SRM_ISD
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.15502 Zobrazit plný text záznamu View this record from Arxiv