Recent advancements in sensor technology have reflected promise in collaborative utilization; specifically, multisource remote sensing data correspondence identification attracts increasing attention. In this article, a domain-transfer learning based generative correspondence analysis (DT-GCA) scheme is proposed, which enables identifying corresponding data in optical and synthetic aperture radar (SAR) images with small-sized reference data. In the proposed architecture, an adversarial domain-translator is investigated as general-purpose domain transference solution to learn cross domain features. The optical-aided implicit representation, which is regarded as the clone of SAR, is adopted to estimate the correlation with SAR images. Particularly, the designed GCA integrates optical-generated features with SAR tightly instead of treating them separately and eliminates the discrepancy influence of different sensors. Experiments on cross-domain remote sensing data are validated, and extensive results demonstrate that the proposed DT-GCA yields substantial improvements over some state-of-the-art techniques when only limited training samples are available.

中文翻译：

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具有有限训练标签的光学和 SAR 数据对应识别的迁移学习

传感器技术的最新进展反映了协作利用的前景；具体而言，多源遥感数据对应识别越来越受到关注。在本文中，提出了一种基于域转移学习的生成对应分析 (DT-GCA) 方案，该方案能够使用小尺寸参考数据识别光学和合成孔径雷达 (SAR) 图像中的相应数据。在所提出的架构中，对抗性域翻译器被研究为通用域转移解决方案以学习跨域特征。采用光辅助隐式表示作为SAR的克隆，用于估计与SAR图像的相关性。特别，设计的 GCA 将光学生成特征与 SAR 紧密集成，而不是单独处理它们，并消除了不同传感器的差异影响。跨域遥感数据的实验得到验证，大量结果表明，当只有有限的训练样本可用时，所提出的 DT-GCA 比一些最先进的技术产生了实质性的改进。