DeepCL: Deep Change Feature Learning on Remote Sensing Images in the
  Metric Space
Haonan Guo Bo Du Chen Wu Chengxi Han Liangpei Zhang
Change detection (CD) is an important yet challenging task in the Earth
observation field for monitoring Earth surface dynamics. The advent of deep
learning techniques has recently propelled automatic CD into a technological
revolution. Nevertheless, deep learning-based CD methods are still plagued by
two primary issues: 1) insufficient temporal relationship modeling and 2)
pseudo-change misclassification. To address these issues, we complement the
strong temporal modeling ability of metric learning with the prominent fitting
ability of segmentation and propose a deep change feature learning (DeepCL)
framework for robust and explainable CD. Firstly, we designed a hard
sample-aware contrastive loss, which reweights the importance of hard and
simple samples. This loss allows for explicit modeling of the temporal
correlation between bi-temporal remote sensing images. Furthermore, the modeled
temporal relations are utilized as knowledge prior to guide the segmentation
process for detecting change regions. The DeepCL framework is thoroughly
evaluated both theoretically and experimentally, demonstrating its superior
feature discriminability, resilience against pseudo changes, and adaptability
to a variety of CD algorithms. Extensive comparative experiments substantiate
the quantitative and qualitative superiority of DeepCL over state-of-the-art CD
approaches.