基于扩散变换器（DiTs）的文本到图像生成已取得显著进展，但遥感（RS）合成滞后于两个障碍：缺乏领域专用的DiT先验和训练大分辨率的高昂成本。通过旋转位置嵌入（RoPE）重标定的无训练分辨率提升提供了解决方案，但现有方法在整个去噪过程中应用静态位置缩放规则。这种统一压缩对遥感图像尤其有害，因其密集的中高频能量编码了关键的空中场景真实感细节，如车辆、建筑轮廓和道路标记。解决这两个挑战需要领域专用的生成先验和去噪感知的位置适应策略。为此，我们微调FLUX在超过10万张精选的遥感图像上，构建强领域先验（RS-FLUX），并提出Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion（SHARP），一种无训练方法，引入有理分数时间调度k_rs(t)到RoPE。SHARP在早期布局形成阶段应用强位置提升，并在细节恢复过程中逐步放松，使外推强度与扩散去噪的频率渐进性质对齐。其分辨率无关的公式进一步使单组超参数支持稳健的多尺度生成。在六个正方形和矩形分辨率的广泛实验中，SHARP在CLIP分数、审美分数和HPSv2上一致优于所有无训练基线，在更激进的外推因子下优势扩大，且计算开销 negligible。代码和权重可在https://github.com/bxuanz/SHARP获取。
英文摘要：
Text-to-image synthesis for remote sensing (RS) lacks an accessible, high-performance generative foundation, as directly training diffusion models at large, high resolutions is computationally prohibitive. Training-free resolution promotion via Rotary Position Embedding (RoPE) extrapolation offers an efficient alternative, but existing algorithms apply static scaling rules tailored to natural scenes, whereas RS imagery is dominated by dense, minute instances that hinge on high-frequency structural integrity. We present a comprehensive framework for large-scale RS synthesis. First, we curate over 100{,}000 image-text pairs to train RS-FLUX, a domain-specialized generative prior. Second, we propose \textbf{SHARP}, a training-free extrapolation algorithm that introduces a Rational Decay Scheduler to modulate RoPE frequencies throughout denoising: strong positional extrapolation early on enforces coherent global layouts, and progressive relaxation later recovers dense high-frequency details. Extensive experiments show that SHARP consistently achieves state-of-the-art performance across multiple promoted resolutions with negligible ($<$4\%) overhead, offering an efficient and structurally faithful solution for large-scale RS generation.
Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（教育部多媒体可信感知与高效计算重点实验室，厦门大学）；ByteDance（字节跳动）；The Chinese University of Hong Kong（香港中文大学）；Duke University（杜克大学）；University of Washington（华盛顿大学）
School of Information and Artificial Intelligence, Yunnan University of Finance and Economics（云南财经大学信息与人工智能学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）
DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection
Min Sun School of Materials Science and Engineering Hainan University（孙敏 海南大学材料科学与工程学院）；Fenghui Guo School of Materials Science and Engineering Hainan University（郭峰 海南大学材料科学与工程学院）
A Spatial-Spectral-Frequency Interactive Network for Multimodal Remote Sensing Classification
Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；Beijing Key Laboratory of Fractional Signals and Systems, Beijing Institute of Technology（北京理工大学分数域信号与系统北京市重点实验室）；School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Key Laboratory of Collaborative Intelligent Systems of Ministry of Education, Xidian University（西安电子科技大学教育部协同智能系统重点实验室）；Academy of Artificial Intelligence, Inner Mongolia Normal University（内蒙古师范大学人工智能研究院）
Frequency-Adaptive Pan-Sharpening with Mixture of Experts
Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）；University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）
Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
The Hong Kong Polytechnic University（香港理工大学）；Wuhan University（武汉大学）；Changjiang Spatial Information Technology Engineering Co., Ltd.（长江空间信息技术工程有限公司）；Peng Cheng Laboratory（鹏城实验室）
Instance segmentation of buildings using keypoints
German Aerospace Center (DLR)（德国航空航天中心）；Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）；Technical University of Munich (TUM)（慕尼黑工业大学）
P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（中国石油大学（华东）海洋与空间信息学院）；School of Surveying and Geoinformation Engineering, East China University of Technology（东华理工大学测绘与地理信息工程学院）
Building Extraction from Remote Sensing Imagery under Hazy and Low-light Conditions: Benchmark and Baseline
MOE Key Lab of ICSP（教育部ICSP重点实验室）；Anhui Provincial Key Lab of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；IMIS Lab of Anhui Province（安徽省IMIS实验室）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
Seabed-Net: A multi-task network for joint bathymetry estimation and seabed classification from remote sensing imagery in shallow waters
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin（技术大学柏林电子工程与计算机科学学院）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing
School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）；School of Automation and Electronic Information, Xiangtan University（湘潭大学自动化与电子信息学院）
