Remote sensing change detection (RSCD) aims to identify the changes of
interest in a region by analyzing multi-temporal remote sensing images, and has
an outstanding value for local development monitoring. Existing RSCD methods
are devoted to contextual modeling in the spatial domain to enhance the changes
of interest. Despite the satisfactory performance achieved, the lack of
knowledge in the frequency domain limits the further improvement of model
performance. In this paper, we propose DDLNet, a RSCD network based on
dual-domain learning (i.e., frequency and spatial domains). In particular, we
design a Frequency-domain Enhancement Module (FEM) to capture frequency
components from the input bi-temporal images using Discrete Cosine Transform
(DCT) and thus enhance the changes of interest. Besides, we devise a
Spatial-domain Recovery Module (SRM) to fuse spatiotemporal features for
reconstructing spatial details of change representations. Extensive experiments
on three benchmark RSCD datasets demonstrate that the proposed method achieves
state-of-the-art performance and reaches a more satisfactory
accuracy-efficiency trade-off. Our code is publicly available at
this https URL

提出基于双域学习（频域和空域）的 RSCD 网络 DDLNet，利用离散余弦变换（DCT）捕捉时频信息以增强感兴趣的变化，并通过空间域恢复模块（SRM）融合时空特征来重建空域变化表示，实验证明该方法在三个基准 RSCD 数据集上实现了最先进的性能，并达到了更为满意的准确率和效率平衡。

DDLNet：用双域学习增强遥感变化检测

DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain  Learning

Fine urban change segmentation using multi-temporal remote sensing images is
essential for understanding human-environment interactions. Despite advances in
remote sensing data for urban monitoring, coarse-grained classification systems
and the lack of continuous temporal observations hinder the application of deep
learning to urban change analysis. To address this, we introduce FUSU, a
multi-source, multi-temporal change segmentation dataset for fine-grained urban
semantic understanding. FUSU features the most detailed land use classification
system to date, with 17 classes and 30 billion pixels of annotations. It
includes bi-temporal high-resolution satellite images with 20-50 cm ground
sample distance and monthly optical and radar satellite time series, covering
847 km2 across five urban areas in China. The fine-grained pixel-wise
annotations and high spatial-temporal resolution data provide a robust
foundation for deep learning models to understand urbanization and land use
changes. To fully leverage FUSU, we propose a unified time-series architecture
for both change detection and segmentation and benchmark FUSU on various
methods for several tasks. Dataset and code will be available at:
this https URL

利用多时相遥感图像进行精细城市变化分割对于理解人地互动非常重要。我们介绍了 FUSU，这是一个多源、多时序变化分割数据集，用于精细级城市语义理解。FUSU 拥有迄今为止最详细的土地利用分类系统，具有 17 类和 300 亿像素的注释。通过提供像素级细化注释和高空间 - 时间分辨率数据，FUSU 为深度学习模型理解城市化和土地利用变化提供了坚实的基础。我们提出了一个统一的时序架构来检测和分割变化，并在多项任务上对 FUSU 进行了基准测试。数据集和代码将在此 URL 上提供。

FUSU：一份细粒度城市语义理解的多时序土地利用变化分割数据集

FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for  Fine-grained Urban Semantic Understanding

As an important task in remote sensing image analysis, remote sensing change
detection (RSCD) aims to identify changes of interest in a region from
spatially co-registered multi-temporal remote sensing images, so as to monitor
the local development. Existing RSCD methods usually formulate RSCD as a binary
classification task, representing changes of interest by merely feature
concatenation or feature subtraction and recovering the spatial details via
densely connected change representations, whose performances need further
improvement. In this paper, we propose STNet, a RSCD network based on spatial
and temporal feature fusions. Specifically, we design a temporal feature fusion
(TFF) module to combine bi-temporal features using a cross-temporal gating
mechanism for emphasizing changes of interest; a spatial feature fusion module
is deployed to capture fine-grained information using a cross-scale attention
mechanism for recovering the spatial details of change representations.
Experimental results on three benchmark datasets for RSCD demonstrate that the
proposed method achieves the state-of-the-art performance. Code is available at
this https URL

本篇论文提出了一种基于时空特征融合的 RSCD 网络 STNet，通过时间特征融合模块和空间特征融合模块强化感兴趣的变化并恢复变化表征的空间细节，实验表明该方法在三个基准数据集上取得了最先进的性能。