Research Article
SAR Image Compression Based on Low-Frequency Suppression and Target Perception
@INPROCEEDINGS{10.1007/978-3-031-71716-1_5, author={Jiawen Deng and Lijia Huang and Yifan Wu}, title={SAR Image Compression Based on Low-Frequency Suppression and Target Perception}, proceedings={Machine Learning and Intelligent Communication. 8th EAI International Conference, MLICOM 2023, Beijing, China, December 17, 2023, Proceedings}, proceedings_a={MLICOM}, year={2024}, month={9}, keywords={Lossy compression Image learning compression Low-frequency suppression Target perception Synthetic Aperture Radar (SAR) image compression}, doi={10.1007/978-3-031-71716-1_5} }- Jiawen Deng
Lijia Huang
Yifan Wu
Year: 2024
SAR Image Compression Based on Low-Frequency Suppression and Target Perception
MLICOM
Springer
DOI: 10.1007/978-3-031-71716-1_5
Abstract
Synthetic Aperture Radar (SAR) images are widely used in the field of remote sensing. To store and transmit the growing amount of SAR image data, more efficient compression algorithms are required. In this paper, a new framework for compressing SAR images is proposed based on deep learning. Firstly, we propose a new two-stage transformation based on low-frequency suppression of input data to achieve high information entropy and low quantization loss. To explore the redundancy between regions of interest and regions of non-interest, an image compression model for target perception is proposed to convert the input SAR image into a compact latent representation together with the target perception map. To evaluate the performance of the algorithm, we conducted experiments on SAR image dataset. The results show that the proposed algorithm is significantly better than the traditional processing algorithm in terms of data input, and can effectively preserve and improve the performance of the image compression model.
