Recently, reference-based image super-resolution (RefSR) has shown excellent
performance in image super-resolution (SR) tasks. The main idea of RefSR is to
utilize additional information from the reference (Ref) image to recover the
high-frequency components in low-resolution (LR) images. By transferring
relevant textures through feature matching, RefSR models outperform existing
single image super-resolution (SISR) models. However, their performance
significantly declines when a domain gap between Ref and LR images exists,
which often occurs in real-world scenarios, such as satellite imaging. In this
letter, we introduce a Domain Matching (DM) module that can be seamlessly
integrated with existing RefSR models to enhance their performance in a
plug-and-play manner. To the best of our knowledge, we are the first to explore
Domain Matching-based RefSR in remote sensing image processing. Our analysis
reveals that their domain gaps often occur in different satellites, and our
model effectively addresses these challenges, whereas existing models struggle.
Our experiments demonstrate that the proposed DM module improves SR performance
both qualitatively and quantitatively for remote sensing super-resolution
tasks.

利用领域匹配（DM）模块改进了参考图像超分辨率（RefSR）模型，在遥感图像超分辨率任务中取得了定性和定量上的性能提升。

跨领域对齐：一种用于遥感图像基于参考的超分辨率的简单领域匹配方法

Bridging the Domain Gap: A Simple Domain Matching Method for  Reference-based Image Super-Resolution in Remote Sensing

The rapid advancement in high-resolution satellite remote sensing data
acquisition, particularly those achieving submeter precision, has uncovered the
potential for detailed extraction of surface architectural features. However,
the diversity and complexity of surface distributions frequently lead to
current methods focusing exclusively on localized information of surface
features. This often results in significant intraclass variability in boundary
recognition and between buildings. Therefore, the task of fine-grained
extraction of surface features from high-resolution satellite imagery has
emerged as a critical challenge in remote sensing image processing. In this
work, we propose the Feature Aggregation Network (FANet), concentrating on
extracting both global and local features, thereby enabling the refined
extraction of landmark buildings from high-resolution satellite remote sensing
imagery. The Pyramid Vision Transformer captures these global features, which
are subsequently refined by the Feature Aggregation Module and merged into a
cohesive representation by the Difference Elimination Module. In addition, to
ensure a comprehensive feature map, we have incorporated the Receptive Field
Block and Dual Attention Module, expanding the receptive field and intensifying
attention across spatial and channel dimensions. Extensive experiments on
multiple datasets have validated the outstanding capability of FANet in
extracting features from high-resolution satellite images. This signifies a
major breakthrough in the field of remote sensing image processing. We will
release our code soon.

通过提出 Feature Aggregation Network (FANet) 方法，本研究突破了高分辨率卫星遥感图像处理中细粒度表面特征提取的关键技术挑战。

高分辨率遥感影像建筑物提取的特征聚合网络

Feature Aggregation Network for Building Extraction from High-resolution  Remote Sensing Images

Synthetic datasets, recognized for their cost effectiveness, play a pivotal
role in advancing computer vision tasks and techniques. However, when it comes
to remote sensing image processing, the creation of synthetic datasets becomes
challenging due to the demand for larger-scale and more diverse 3D models. This
complexity is compounded by the difficulties associated with real remote
sensing datasets, including limited data acquisition and high annotation costs,
which amplifies the need for high-quality synthetic alternatives. To address
this, we present SyntheWorld, a synthetic dataset unparalleled in quality,
diversity, and scale. It includes 40,000 images with submeter-level pixels and
fine-grained land cover annotations of eight categories, and it also provides
40,000 pairs of bitemporal image pairs with building change annotations for
building change detection task. We conduct experiments on multiple benchmark
remote sensing datasets to verify the effectiveness of SyntheWorld and to
investigate the conditions under which our synthetic data yield advantages. We
will release SyntheWorld to facilitate remote sensing image processing
research.

SyntheWorld 是一种无与伦比的合成数据集，具有高质量、多样性和规模，包含 40,000 张具有亚米级像素的图像和八个类别的细粒度土地覆盖注释，还提供了 40,000 对双时间图像对和建筑变化注释用于建筑变化检测任务，我们在多个基准遥感数据集上进行了实验，验证了 SyntheWorld 的有效性并调研了合成数据在何种情况下具有优势，我们将发布 SyntheWorld 以促进遥感图像处理研究。