Semi-Supervised Change Detection with Multi-View Feature Enhancement

Change detection (CD) plays a crucial role in various physical applications. Recently, many studies have turned to semi-supervised semantic segmentation methods to alleviate the reliance on extensive annotations. However, these methods tend to neglect the presence of the cross-temporal background noise in bi-temporal remote sensing image (RSI) pairs, which can mislead model’s prediction. Therefore, this work introduces an innovative Multi-view Feature Enhancement (MFE) method for semi-supervised CD. To mitigate the influence of inherent cross-temporal background noise in RSI pairs, we design a multi-view strategy and temporal-perception data augmentation for feature enhancement. According to the experimental results on two publicly available datasets, our proposed method outperforms several state-of-the-art (SOTA) methods in terms of intersection over union (IoU) and overall accuracy (OA).