Self-Supervised Learning of Contrast-Diffusion Models for Land Cover Classification in SAR Images

Deep learning (DL) methods have been widely applied to synthetic aperture radar (SAR) land cover classification. The complexity of SAR data and the limited availability of labeled samples greatly constrain the feature learning and the generalization ability of the model. Inspired by the excellent generative performance of denoising diffusion probabilistic models (DDPMs) on complex data distributions, a self-supervised learning framework based on contrast-diffusion models (CDMs) is proposed to expand the applicability to multiple broad scenarios with complex and varying imaging conditions under limited annotated data conditions. Specially, the proposed framework consists of the upstream CDM pretraining on all unlabeled samples and the downstream land cover classification with few labeled samples in each test scene. Concretely, in the upstream task, the features are captured through the generative learning of the DDPM. Following this, the dimensionality reduction and resolution expansion (DRRE) module is designed and embedded to reduce feature redundancy and align the feature granularity between layers and the input image. Finally, contrastive learning is employed to enforce semantic feature consistency across different steps. In the downstream task, the feature in pretrained CDM is efficiently delivered in a single-step reverse diffusion process and then fine-tuned with few labeled samples from each test scene and finally output the predictions. Compared with several supervised and self-supervised methods, the proposed framework achieves superior classification and generalization performance on multiple broad scenes with complex and varying imaging conditions. For example, based on the average results from six test scenes, CDM shows improvements in overall accuracy (OA) of 41.42%, 34.40%, 7.70%, 8.53%, and 9.60% compared to DeepLabv3+, CCNR, SegFormer, MAE, and DDPM, respectively. The code is available at https://github.com/gosling123456/CDM.git