|Table of Contents|

S2OGAN: A SAR-optical Image Generative Translation Method for Texture and Edge Fidelity(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2025年第04期
Page:
806-828
Research Field:
黄河流域生态保护和高质量发展专刊(下)
Publishing date:

Info

Title:
S2OGAN: A SAR-optical Image Generative Translation Method for Texture and Edge Fidelity
Author(s):
DING Ming-tao1; 2; 3; 4; 5*; LU Zhao-long1; 3; LI Zhen-hong1; 2; 3; 4; JIANG Hui1; 3; HUANG Wu-biao6
(1. School of Geological Engineering and Geomatics, Chang' an University, Xi'an 710054, Shaanxi, China; 2. State Key Laboratory of Loess Science, Xi'an 710054, Shaanxi, China; 3. Big Data Center for Geosciences and Satellites, Chang'an University, Xi'an 710054, Shaanxi, China; 4. Key Laboratory of Ecological Geology and Disaster Prevention of Ministry of Natural Resources, Chang'an University, Xi'an 710054, Shaanxi, China; 5. Key Laboratory of Smart Earth, Beijing 100029, China; 6. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, Hubei, China)
Keywords:
S2OGAN;  image translation;  cGAN;  texture fidelity;  edge fidelity;  SAR image;  glacier;  Sanjiangyuan region
PACS:
P237
DOI:
10.19814/j.jese.2024.10023
Abstract:
Optical remote sensing imagery, characterized by its rich texture and spectral information, has become a valuable data resource. However, its acquisition is highly susceptible to variations in illumination and atmospheric conditions. In particular, adverse weather scenarios such as cloud cover, fog, or heavy precipitation often result in data loss or reduced image quality, which constrains its effectiveness in continuous spatiotemporal monitoring and application performance. In comparison, synthetic aperture radar(SAR)offers all-weather, day-and-night imaging capabilities, and can effectively compensate for the limitations of optical remote sensing imagery. To enhance the continuity and completeness of remote sensing monitoring, a SAR-to-optical generative translation method, named as S2OGAN, is proposed based on the conditional generative adversarial network(cGAN)framework, targeting the common issue of scattering noise in SAR images. The S2OGAN incorporates a denoising convolutional neural network(DnCNN)as a dedicated denoising module to effectively suppress noise and improve texture fidelity; additionally, the histogram of phase coherence(HOPC)is introduced as an edge loss to enhance the representation of edge features, thereby achieving more accurate reconstruction of both texture and structural information; to evaluate the effectiveness of S2OGAN in glacier scenarios, a dedicated glacier observation experimental dataset is constructed using the GEE platform. Experimental results on several widely used datasets demonstrate that S2OGAN outperforms the classical image translation methods including Pix2Pix, CycleGAN, CUT and Semi-I2I in terms of overall performance; under varying spatial resolutions, S2OGAN exhibits greater stability than the above four methods across multiple quantitative evaluation metrics, highlighting its superior robustness; in simple scenario, the above five methods tend to perform better than that in complex scenario; however, S2OGAN not only achieves the best overall performance, but also maintains relatively stable results in complex scenario; in glacier observation tasks, the structural similarity index(SSIM)of images translated by S2OGAN reaches 0.668, effectively supporting the quantitative delineation of ice tongue boundaries of glacier; nevertheless, certain differences remain between the translated texture details and those of true-color optical remote sensing images. Finally, based on the translated image, the ablation areas of two ice tongues of Geladandong glacier in Sanjiangyuan region are estimated to be approximately 0.346 3 and 0.089 0 km2, respectively. These results demonstrate the potential of S2OGAN as a new technical approach for continuous remote sensing monitoring in meteorologically challenging regions.

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Last Update: 2025-07-25