@inproceedings{1d6f3895487c4768b349d0e1dc82b0fe,
title = "Feature Learning and SAR Imaging Method Based on Convolution Neural Network",
abstract = "Synthetic Aperture Radar (SAR) can perform all-time and all-weather observations and is wildly used in earth remote sensing. The sparsity-driven SAR imaging methods can reconstruct sparse scenes under down-sampling conditions, but they are unsuitable for non-sparse scenes. To reconstruct non-sparse scenes from under-sampled data and further improve the utilization efficiency of sampled data, this paper proposes a feature learning and SAR imaging method and implements it through a deep network. The imaging model is constructed firstly, where a feature-based sparsity regularization term is incorporated. Then, by unfolding the iterative solution derived via the Alternating Direction Multiplier Method (ADMM) algorithm, a CNN-based deep network is proposed to solve this imaging model. In the proposed network, convolution layers are used to represent and learn the scene feature prior knowledge. Simulation experiments verify the effectiveness of the proposed method.",
keywords = "ADMM, SAR imaging, convolution neural network, feature learning, unfolding",
author = "Weibo Huo and Min Li and Junjie Wu and Zhongyu Li and Jianyu Yang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IGARSS46834.2022.9884789",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2959--2962",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
}