研究方向
1.针对山区、湿地与河口等区域的灾害制图、灾害监测及灾害预警等地理学理论研究与应用。
2.针对城市与海岸带等区域的变化检测、语义分类及目标追踪等遥感数据处理方法理论研究与应用。
3.基于多类、多源高分辨率遥感数据的机器学习、深度学习及迁移学习等计算机视觉与模式识别相关技术研究与应用。
代表性成果
Fang, B., Chen, G., Kou, R., Paoletti, M.E., Haut, J.M., Plaza, A., 2023. CIT: Content-invariant translation with hybrid attention mechanism for unsupervised change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 204, 321-339.
Fang, B., Chen, G., Chen, J., Ouyang, G., Kou, R., Wang, L., 2021. CCT: Conditional co-training for truly unsupervised remote sensing image segmentation in coastal areas. Remote Sensing, 13 (17), 3521.
Fang, B., Chen, G., Ouyang, G., Chen, J., Kou, R., Wang, L., 2021. Content-invariant dual learning for change detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17.
Fang, B., Chen, G., Pan, L., Kou, R., Wang, L., 2020. GAN-based Siamese framework for landslide inventory mapping using bi-temporal optical remote sensing images. IEEE Geoscience and Remote Sensing Letters, 18 (13), 391-395.
Fang, B., Kou, R., Pan, L., Chen, P., 2019. Category-sensitive domain adaptation for land cover mapping in aerial scenes. Remote Sensing, 11 (22), 2631.
Fang, B., Pan, L., Kou, R., 2019. Dual learning-based Siamese framework for change detection using bi-temporal VHR optical remote sensing images. Remote Sensing, 11 (11), 1292.
Kou, R., Fang, B., Chen, G., Wang, L., 2020. Progressive domain adaptation for change detection using season-varying remote sensing images. Remote Sensing, 12 (22), 3815.