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 (https://github.com/ShizhenChang/Dsfer-Net).

翻译：变化检测作为高分辨率遥感图像的重要应用，旨在监测和分析地表随时间的变化。随着高分辨率遥感数据量的快速增长及纹理特征的复杂化，大量基于深度学习的定量方法已被提出。尽管这些方法通过提取深层特征并融合时空信息，优于传统变化检测方法，但关于深层特征如何提升检测性能的合理解释仍较为缺乏。本研究发现，现代Hopfield网络层在语义理解方面表现优异。为此，本文提出一种深度监督与特征检索网络（Dsfer-Net）用于双时相变化检测。具体而言，通过全卷积孪生网络联合提取双时相图像的高代表性深层特征；基于双时相图像的时序地理信息，我们设计了特征检索模块，以深度监督的方式检索差异特征并利用判别性信息。同时发现，深度监督特征检索模块为所提网络深层语义理解提供了可解释性证据。最终，该端到端网络通过聚合不同层的检索特征与特征对，构建了新颖框架。在三个公开数据集（LEVIR-CD、WHU-CD和CDD）上的实验证实了Dsfer-Net相较于其他前沿方法的优越性。代码将开源（https://github.com/ShizhenChang/Dsfer-Net）。