Change detection, as an important application for high-resolution remote
sensing images, aims to monitor and analyze changes in the land surface over
time. With the rapid growth in the quantity of high-resolution remote sensing
data and the complexity of texture features, a number of quantitative deep
learning-based methods have been proposed. Although these methods outperform
traditional change detection methods by extracting deep features and combining
spatial-temporal information, reasonable explanations about how deep features
work on improving the detection performance are still lacking. In our
investigations, we find that modern Hopfield network layers achieve
considerable performance in semantic understandings. In this paper, we propose
a Deep Supervision and FEature Retrieval network (Dsfer-Net) for bitemporal
change detection. Specifically, the highly representative deep features of
bitemporal images are jointly extracted through a fully convolutional Siamese
network. Based on the sequential geo-information of the bitemporal images, we
then design a feature retrieval module to retrieve the difference feature and
leverage discriminative information in a deeply supervised manner. We also note
that the deeply supervised feature retrieval module gives explainable proofs
about the semantic understandings of the proposed network in its deep layers.
Finally, this end-to-end network achieves a novel framework by aggregating the
retrieved features and feature pairs from different layers. Experiments
conducted on three public datasets (LEVIR-CD, WHU-CD, and CDD) confirm the
superiority of the proposed Dsfer-Net over other state-of-the-art methods. Code
will be available online (this https URL).

本文提出了一种新的基于深度学习的双时相遥感图像变化检测方法，使用全卷积连接的孪生网络进行高质量的特征提取，利用特征检索模块在深度监督下检索差异特征和利用其语义信息来设计更具可解释性的检测网络。在三个公共数据集上的实验结果表明，该方法明显优于现有的其他方法。