{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T19:34:43Z","timestamp":1767900883775,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41961053"],"award-info":[{"award-number":["41961053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31860182"],"award-info":[{"award-number":["31860182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202101AT070102"],"award-info":[{"award-number":["202101AT070102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202101BE070001-037"],"award-info":[{"award-number":["202101BE070001-037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202201AT070164"],"award-info":[{"award-number":["202201AT070164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yunnan Fundamental Research Projects","award":["41961053"],"award-info":[{"award-number":["41961053"]}]},{"name":"Yunnan Fundamental Research Projects","award":["31860182"],"award-info":[{"award-number":["31860182"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202101AT070102"],"award-info":[{"award-number":["202101AT070102"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202101BE070001-037"],"award-info":[{"award-number":["202101BE070001-037"]}]},{"name":"Yunnan Fundamental Research Projects","award":["202201AT070164"],"award-info":[{"award-number":["202201AT070164"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the existing methods for SAR-to-optical translation have two main drawbacks. First, single generators usually sacrifice either structure or texture features to balance the model performance and complexity, which often results in textural or structural distortion; second, due to large nonlinear radiation distortions (NRDs) in SAR images, there are still visual differences between the pseudo-optical images generated by current generative adversarial networks (GANs) and real optical images. Therefore, we propose a dual-generator translation network for fusing structure and texture features. On the one hand, the proposed network has dual generators, a texture generator, and a structure generator, with good cross-coupling to obtain high-accuracy structure and texture features; on the other hand, frequency-domain and spatial-domain loss functions are introduced to reduce the differences between pseudo-optical images and real optical images. Extensive quantitative and qualitative experiments show that our method achieves state-of-the-art performance on publicly available optical and SAR datasets. Our method improves the peak signal-to-noise ratio (PSNR) by 21.0%, the chromatic feature similarity (FSIMc) by 6.9%, and the structural similarity (SSIM) by 161.7% in terms of the average metric values on all test images compared with the next best results. In addition, we present a before-and-after translation comparison experiment to show that our method improves the average keypoint repeatability by approximately 111.7% and the matching accuracy by approximately 5.25%.<\/jats:p>","DOI":"10.3390\/rs14122946","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T04:39:55Z","timestamp":1655786395000},"page":"2946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Dual-Generator Translation Network Fusing Texture and Structure Features for SAR and Optical Image Matching"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9229-6109","authenticated-orcid":false,"given":"Han","family":"Nie","sequence":"first","affiliation":[{"name":"Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4150-4104","authenticated-orcid":false,"given":"Zhitao","family":"Fu","sequence":"additional","affiliation":[{"name":"Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1918-5346","authenticated-orcid":false,"given":"Bo-Hui","family":"Tang","sequence":"additional","affiliation":[{"name":"Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650031, China"},{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4829-811X","authenticated-orcid":false,"given":"Ziqian","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650031, China"}]},{"given":"Sijing","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2962-1508","authenticated-orcid":false,"given":"Leiguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650024, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.inffus.2020.01.003","article-title":"Pixel Level Fusion Techniques for SAR and Optical Images: A Review","volume":"59","author":"Kulkarni","year":"2020","journal-title":"Inf. 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