航空学报2025，Vol.46Issue(7)：293-307,15.DOI:10.7527/S1000-6893.2024.31003
基于地貌类别信息指导的SAR图像仿真方法
SAR image simulation method guided by geomorphic category information
摘要
Abstract
The current deep learning SAR image simulation methods generally do not consider the feature differences of different geomorphic categories in SAR images,resulting in distortion of geomorphic differentiation in simulated im-ages.To address this issue,this paper proposes a visible-to-SAR image translation algorithm guided by geomorphic category information.A topographic category extraction branch is designed,and the attention mechanism is used to collect topographic category information from multiple dimensions to guide SAR image simulation.Image content ex-traction branches are designed,and contrast learning is used to enhance the feature extraction capability of the net-work for common content information of visible light and SAR images.An image generation module is designed to con-vert content information into SAR images under the guidance of geomorphic category information,so that the gener-ated SAR images have the features corresponding to geomorphic categories,and path regularization is used to subdi-vide the complete translation process from visible light to SAR images to reduce the difficulty of implementation.A pair dataset of visible light and SAR images with different terrains is established.Experimental comparison of 6 evaluation indexes shows that the proposed algorithm has better performance than other representative algorithms,with the structural similarity being improved by at least 9.24%.In addition,the simulated SAR image shows a higher degree of realism in the visual effect,and can effectively retain the features of landform categories.关键词
合成孔径雷达/深度学习/多类别地貌/注意力机制/对比学习/路径正则化Key words
SAR/deep learning/multi-category terrain/attention mechanism/contrastive learning/path regularization分类
航空航天引用本文复制引用
孟令捷,李红光,李新军..基于地貌类别信息指导的SAR图像仿真方法[J].航空学报,2025,46(7):293-307,15.基金项目
国家自然科学基金(62076019) （62076019）
国家重点研发计划(2022YFB3904303) National Natural Science Foundation of China(62076019) （2022YFB3904303）
National Key Research and Development Program of China(2022YFB3904303) （2022YFB3904303）
