Explainable SAR

Explainable Deep Learning for SAR Data

Deep learning have gains great success for SAR Image interpretation tasks but they are black box models the could not tell what they learn from data, how it makes decision, etc. To make more physically interpretable and transparent models, recently we propose a contrastive-regulated convolutional neural network (CNN) in the complex domain, leading to learn a physically interpretable deep learning model directly from the original backscattered data.

J. Zhao, M. Datcu, Z. Zhang, H. Xiong and W. Yu, "Contrastive-Regulated CNN in the Complex Domain: A Method to Learn Physical Scattering Signatures From Flexible PolSAR Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 12, pp. 10116-10135, Dec. 2019.

郭炜炜, 张增辉, 郁文贤, 等. SAR图像目标识别的可解释性问题探讨[J]. 雷达学报, 2020, 9(3): 462–476. doi: 10.12000/JR20059