Learning Remote Sensing Aleatoric Uncertainty for Semi-Supervised Change Detection

Significant progress has been recently achieved in the field of remote sensing image (RSI) change detection based on data-driven deep learning. Fully supervised models have limitations on the availability of massive annotated training data, while semi-supervised change detection (SSCD) has garnered increasingly widespread attention. Nevertheless, existing SSCD methods do not categorize the types of remote sensing aleatoric uncertainty (RSAU), let alone investigate the impact of uncertainty on performance. To this end, we define RSAU for SSCD and introduce the progressive uncertainty-aware and uncertainty-guided framework (PUF). It consists of two crucial components to perceive and guide the RSAU in the training stage. The first component, i.e., progressive uncertainty-aware learning (PUAL), decodes and quantifies the uncertainty inherent in the samples from the weak branch. The second one, i.e., uncertainty-guided multiview learning (UML), generates multiple image pairs designed for distortion and mixing for the strong branch. UML utilizes the uncertainty values derived from PUAL to offer guidance throughout the training process, which discerns and learns discriminative features from high-quality samples. Extensive experiments are conducted on three multiclass and building change detection (CD) benchmarks, i.e., CDD, SYSU, and LEVIR-CD. Furthermore, we propose a small dataset to enhance the understanding of aleatoric uncertainty, namely, LEVIR-AU. The proposed PUF consistently achieves state-of-the-art (SOTA) performance. The dataset and codes are available at https://github.com/shenjh0/PUF.