Bi-modal contrastive learning for crop classification using Sentinel-2 and Planetscope - Patnala, Ankit; Stadtler, Scarlet; Schultz, Martin G.; Gall, Juergen (Frontiers in Remote Sensing, 2024)
Remote sensing has enabled large-scale crop classification for understanding agricultural ecosystems and estimating production yields. In recent years, machine learning has become increasingly relevant for automated crop classification. However, the existing algorithms require a huge amount of annotated data. Self-supervised learning, which enables training on unlabeled data, has great potential to overcome the problem of annotation. Contrastive learning, a self-supervised approach based on instance discrimination, has shown promising results in the field of natural as well as remote sensing images. Crop data often consists of field parcels or sets of pixels from small spatial regions. Additionally, one needs to account for temporal patterns to correctly label crops. Hence, the standard approaches for landcover classification cannot be applied. In this work, we propose two contrastive self-supervised learning approaches to obtain a pre-trained model for crop classification without the need for labeled data. First, we adopt the uni-modal contrastive method (SCARF) and, second, we use a bi-modal approach based on Sentinel-2 and Planetscope data instead of standard transformations developed for natural images to accommodate the spectral characteristics of crop pixels. Evaluation in three regions of Germany and France shows that crop classification with the pre-trained multi-modal model is superior to the pre-trained uni-modal method as well as the supervised baseline models in the majority of test cases.