# Contrastive Multiview Coding with Electro-optics for SAR Semantic Segmentation Source: [Contrastive Multiview Coding with Electro-optics for SAR Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:49.464Z Authors: Keumgang Cha, Junghoon Seo, Yeji Choi Published: 2021-08-31T23:55:41.000Z ## Abstract In the training of deep learning models, how the model parameters are initialized greatly affects the model performance, sample efficiency, and convergence speed. Representation learning for model initialization has recently been actively studied in the remote sensing field. In particular, the appearance characteristics of the imagery obtained using the a synthetic aperture radar (SAR) sensor are quite different from those of general electro-optical (EO) images, and thus representation learning is even more important in remote sensing domain. Motivated from contrastive multiview coding, we propose multi-modal representation learning for SAR semantic segmentation. Unlike previous studies, our method jointly uses EO imagery, SAR imagery, and a label mask. Several experiments show that our approach is superior to the existing methods in model performance, sample efficiency, and convergence speed. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Change Detection on Remote Sensing Images (test)](): Change Detection; Remote Sensing Images (test); Trainable Parameters (M): 22.48. ## Original sources and implementation [Original paper]() [PDF]()