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+remote+sensing+are+crucial+for+earth+observation%252C+offering+complementary+capabilities.+While+optical+sensors+provide+high-quality+images%252C+they+are+limited+by+weather+and+lighting+conditions.+In+contrast%252C+SAR+sensors+can+operate+effectively+under+adverse+conditions.+This+letter+proposes+a+GAN-based+SAR-to-optical+image+translation+method+named+Seg-CycleGAN%252C+designed+to+enhance+the+accuracy+of+ship+target+translation+by+leveraging+semantic+information+from+a+pre-trained+semantic+segmentation+model.+Our+method+utilizes+the+downstream+task+of+ship+target+semantic+segmentation+to+guide+the+training+of+image+translation+network%252C+improving+the+quality+of+output+Optical-styled+images.+The+potential+of+foundation-model-annotated+datasets+in+SAR-to-optical+translation+tasks+is+revealed.+This+work+suggests+broader+research+and+applications+for+downstream-task-guided+frameworks.+The+code+will+be+available+at+https%3a%252F%252Fgithub.com%252FNPULHH%252F&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.16238d797c510da168881c00e88aafb7%3frik%3dqBD6FkpbyasZPg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Cyclegan+SAR&simid=607989416836076341&FORM=IRPRST&ck=76321C7660EF7FBF1E57961C8ABFF689&selectedIndex=0&itb=0)
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