Contrastive Self-Supervised Learning With Smoothed Representation for Remote Sensing

In remote sensing, numerous unlabeled images are continuously accumulated over time, and it is difficult to annotate all the data. Therefore, a self-supervised learning technique that can improve the recognition rate using unlabeled data will be useful for remote sensing. This letter presents contrastive self-supervised learning with smoothed representation for remote sensing based on the SimCLR framework. In self-supervised learning for remote sensing, the well-known characteristic that images within a short distance might be semantically similar is usually used. Our algorithm is based on this knowledge, and it simultaneously utilizes several neighboring images as a positive pair of the anchor image, unlike existing methods such as Tile2Vec. Furthermore, MoCo and SimCLR, which are among the state-of-the-art self-supervised learning approaches, only use two augmented views of the single-input image, but our proposed approach uses multiple-input images and averages their representations (e.g., smoothed representation). Consequently, the proposed approach outperforms state-of-the-art self-supervised learning methods, such as Tile2Vec, MoCo, and SimCLR, in the cropland data layer (CDL), RESISC-45, UCMerced, and EuroSAT data sets. The proposed approach is comparable to the pretrained ImageNet model in the CDL classification task.

Automated summary

This letter introduces a contrastive self-supervised learning technique based on the SimCLR framework, which utilizes smoothed representation to enhance recognition in remote sensing.