基于地貌类别信息指导的SAR图像仿真方法

doi:10.7527/S1000-6893.2024.31003

Abstract

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 images. 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 extraction branches are designed， and contrast learning is used to enhance the feature extraction capability of the network for common content information of visible light and SAR images. An image generation module is designed to convert content information into SAR images under the guidance of geomorphic category information， so that the generated SAR images have the features corresponding to geomorphic categories， and path regularization is used to subdivide 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.

Key words: SAR, deep learning, multi-category terrain, attention mechanism, contrastive learning, path regularization

CLC Number:

V249.1

Lingjie MENG, Hongguang LI, Xinjun LI. SAR image simulation method guided by geomorphic category information[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(7): 331003.

Figures/Tables 13

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References 33

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算法	SSIM	FSIM	PSNR	SRE	FID	KID
Pix2pix	0.119	0.469	27.949	49.705	98.220	5.471
Cyclegan	0.098	0.467	27.962	50.060	100.410	3.364
CUT	0.067	0.462	27.940	49.686	165.690	9.216
Santa	0.093	0.456	27.949	49.756	93.237	3.675
UNSB	0.053	0.429	27.917	49.235	94.745	4.400
SRC	0.089	0.454	27.971	50.181	87.861	2.136
本文方法	0.130	0.479	27.972	50.200	56.750	0.671

实验条件	SSIM	FSIM	PSNR	SRE	FID	KID
去掉注意力机制	0.115	0.466	27.960	50.040	90.882	4.011
去掉对比学习	0.121	0.478	27.945	49.922	87.266	3.048
去掉路径正则化	0.111	0.468	27.970	50.084	66.101	1.845
本文方法	0.130	0.479	27.972	50.200	56.750	0.671

算法	SSIM	FSIM	PSNR	SRE	FID	KID
Pix2pix	0.106	0.443	27.831	49.112	104.836	6.241
Cyclegan	0.087	0.441	27.844	49.465	106.728	4.128
CUT	0.059	0.438	27.769	49.051	174.987	10.364
Santa	0.081	0.433	27.811	49.132	100.867	4.512
UNSB	0.047	0.407	27.703	48.891	103.214	5.143
SRC	0.076	0.428	27.845	49.578	96.041	3.186
本文方法	0.117	0.462	27.854	49.789	61.439	1.213

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SAR image simulation method guided by geomorphic category information

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