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Change detection (CD) is an important yet challenging task in the Earthobservation field for monitoring Earth surface dynamics. The advent of deeplearning techniques has recently propelled automatic CD into a technologicalrevolution. Nevertheless, deep learning-based CD methods are still plagued bytwo primary issues: 1) insufficient temporal relationship modeling and 2)pseudo-change misclassification. To address these issues, we complement thestrong temporal modeling ability of metric learning with the prominent fittingability of segmentation and propose a deep change feature learning (DeepCL)framework for robust and explainable CD. Firstly, we designed a hardsample-aware contrastive loss, which reweights the importance of hard andsimple samples. This loss allows for explicit modeling of the temporalcorrelation between bi-temporal remote sensing images. Furthermore, the modeledtemporal relations are utilized as knowledge prior to guide the segmentationprocess for detecting change regions. The DeepCL framework is thoroughlyevaluated both theoretically and experimentally, demonstrating its superiorfeature discriminability, resilience against pseudo changes, and adaptabilityto a variety of CD algorithms. Extensive comparative experiments substantiatethe quantitative and qualitative superiority of DeepCL over state-of-the-art CDapproaches.

DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space