ISPRS Journal of Photogrammetry and Remote Sensing最新文献

英文中文

L2M-Reg: Building-level uncertainty-aware registration of outdoor LiDAR point clouds and semantic 3D city models L2M-Reg：室外激光雷达点云和语义三维城市模型的建筑级不确定性感知配准

IF 12.7 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-06 DOI: 10.1016/j.isprsjprs.2026.02.005

Ziyang Xu, Benedikt Schwab, Yihui Yang, Thomas H. Kolbe, Christoph Holst

引用次数: 0

Adaptive image zoom-in with bounding box transformation for UAV object detection 基于边界盒变换的无人机目标检测自适应图像放大

IF 12.7 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-05 DOI: 10.1016/j.isprsjprs.2026.01.036

Tao Wang, Chenyu Lin, Chenwei Tang, Jizhe Zhou, Deng Xiong, Jianan Li, Jian Zhao, Jiancheng Lv

引用次数: 0

Monitoring global power outages induced by tropical cyclones using nighttime light data 利用夜间灯光数据监测热带气旋引起的全球停电

IF 12.7 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-04 DOI: 10.1016/j.isprsjprs.2026.01.042

Liujun Zhu, Yaqian Li, Shanshui Yuan, Shi Shi, Fang Ji

引用次数: 0

A novel transformer-based CO2 retrieval framework incorporating prior constraint and hierarchical features injection: assessment of transferability for Tansat-2 结合先验约束和分层特征注入的基于变压器的CO2检索框架：Tansat-2可转移性评估

IF 12.7 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-02 DOI: 10.1016/j.isprsjprs.2026.01.039

Lingfeng Zhang, Lu Zhang, Xingying Zhang, Tiantao Cheng, Xifeng Cao, Tongwen Li, Dongdong Liu, Yang Zhang, Yuhan Jiang, Ruohua Hu, Haiyang Dou, Lin Chen

引用次数: 0

Comparative assessment of AI-based and classical DSAS approaches in multi-temporal shoreline prediction: A case study of Ras El-Bar coast, Egypt 基于人工智能和经典DSAS方法在多时间线预测中的比较评估：以埃及Ras El-Bar海岸为例

IF 12.7 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-02-01 DOI: 10.1016/j.isprsjprs.2026.01.040

Hesham M. El-Asmar, Mahmoud Sh. Felfla

引用次数: 0

Multi-object tracking of vehicles and anomalous states in remote sensing videos: Joint learning of historical trajectory guidance and ID prediction 遥感视频中车辆多目标跟踪与异常状态：历史轨迹制导与ID预测的联合学习

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-01-31 DOI: 10.1016/j.isprsjprs.2026.01.038

Bin Wang , Yuan Zhou , Haigang Sui , Guorui Ma , Peng Cheng , Di Wang

Research on multi-object tracking (MOT) of vehicles based on remote sensing video data has achieved breakthrough progress. However, MOT of vehicles in complex scenarios and their anomalous states after being subjected to strong deformation interference remains a huge challenge. This is of great significance for military defense, traffic flow management, vehicle damage assessment, etc. To address this problem, this study proposes an end-to-end MOT method that integrates a joint learning paradigm of historical trajectory guidance and identity (ID) prediction, aiming to bridge the gap between vehicle detection and continuous tracking after anomalous states occurrence. The proposed network framework primarily consists of a Frame Feature Aggregation Module (FFAM) that enhances spatial consistency of objects across consecutive video frames, a Historical Tracklets Flow Encoder (HTFE) that employs Mamba blocks to guide object embedding within potential motion flows based on historical frames, and a Semantic-Consistent Clustering Module (SCM) constructed via sparse attention computation to capture global semantic information. The discriminative features extracted by these modules are fused by a Dual-branch Modulation Fusion Unit (DMFU) to maximize the performance of the model. This study also constructs a new dataset for MOT of vehicles and anomalous states in videos, termed the VAS-MOT dataset. Extensive validation experiments conducted on this dataset demonstrate that the method achieves the highest level of performance, with HOTA and MOTA reaching 68.2% and 71.5%, respectively. Additional validation on the open-source dataset IRTS-AG confirms the strong robustness of the proposed method, showing excellent performance in long-term tracking of small vehicles in infrared videos under complex scenarios, where HOTA and MOTA reached 70.9% and 91.6%, respectively. The proposed method provides valuable insights for capturing moving objects and their anomalous states, laying a foundation for further damage assessment.

基于遥感视频数据的车辆多目标跟踪（MOT）研究取得了突破性进展。然而，复杂场景下车辆的MOT及其在受到强变形干扰后的异常状态仍然是一个巨大的挑战。这对军事防御、交通流管理、车辆损伤评估等具有重要意义。为了解决这一问题，本研究提出了一种端到端的MOT方法，该方法集成了历史轨迹引导和身份（ID）预测的联合学习范式，旨在弥合异常状态发生后车辆检测与持续跟踪之间的差距。所提出的网络框架主要由帧特征聚合模块（FFAM）组成，该模块增强了对象在连续视频帧中的空间一致性；历史轨迹流编码器（HTFE）采用曼巴块来指导基于历史帧的潜在运动流中的对象嵌入；以及通过稀疏注意力计算构建的语义一致聚类模块（SCM）来捕获全局语义信息。通过双支路调制融合单元（Dual-branch Modulation Fusion Unit， DMFU）对这些模块提取的判别特征进行融合，使模型的性能最大化。本研究还构建了一个新的车辆MOT和视频异常状态数据集，称为VAS-MOT数据集。在该数据集上进行的大量验证实验表明，该方法达到了最高的性能水平，HOTA和MOTA分别达到了68.2%和71.5%。在开源数据集IRTS-AG上的进一步验证证实了该方法具有较强的鲁棒性，在复杂场景下红外视频中对小型车辆的长期跟踪表现优异，HOTA和MOTA分别达到70.9%和91.6%。该方法为捕获运动物体及其异常状态提供了有价值的见解，为进一步的损伤评估奠定了基础。

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引用次数: 0

A geometric Cross-Propagation-Calibration method for SAR constellation based on the graph theory 基于图论的SAR星座几何交叉传播定标方法

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-01-31 DOI: 10.1016/j.isprsjprs.2026.01.007

Yitong Luo , Xiaolan Qiu , Bei Lin , Zekun Jiao , Wei Wang , Chibiao Ding

The networking capability of SAR constellations can effectively reduce the average revisit period, which has become a new trend in SAR Earth observation. However, the system electronic delay of several or even dozens of SAR satellites in a constellation must be calibrated and monitored for a long time to ensure high geometric accuracy of the product. In this paper, a geometric cross-propagation-calibration method for SAR constellations is proposed, which can calibrate the slant ranges of the SAR satellites in a constellation without any calibrators. The proposed method constructs a graph from all reference and uncalibrated SAR images involved in a cross-calibration task. For each uncalibrated image, the cumulative calibration error along paths originating from the reference images is estimated, enabling the identification of a path that minimizes this error. Cross-calibration is then performed sequentially along this optimal path. A closed-form expression is derived to estimate the cumulative calibration error along any path, which also reveals the underlying mechanism of error propagation in cross-calibration. Experiments based on real data show that the proposed method enables two China’s microsatellites, Qilu-1 and Xingrui-9, to achieve geometric accuracy of less than 5 m after calibration.

SAR星座的组网能力可以有效地缩短平均重访周期，成为SAR对地观测的新趋势。然而，一个星座内的几颗甚至几十颗SAR卫星的系统电子延迟必须经过长时间的校准和监测，才能保证产品的高几何精度。本文提出了一种SAR星座几何交叉传播定标方法，该方法可以在不使用任何定标器的情况下对星座内SAR卫星的倾斜距离进行定标。该方法将交叉校准任务中涉及的所有参考和未校准SAR图像构建成一个图形。对于每个未校准的图像，沿着源自参考图像的路径估计累积校准误差，从而能够识别最小化该误差的路径。然后沿着这条最优路径依次进行交叉校准。推导出沿任意路径估计累计校准误差的封闭表达式，揭示了交叉校准误差传播的潜在机制。基于实际数据的实验表明，所提出的方法使中国两颗微卫星“齐鲁一号”和“星瑞九号”标定后的几何精度达到小于5 m。

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引用次数: 0

Monitoring snow cover dynamics at 30-m resolution in higher latitude regions using Harmonized Landsat Sentinel-2 利用Harmonized Landsat Sentinel-2监测高纬度地区30米分辨率积雪动态

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-01-31 DOI: 10.1016/j.isprsjprs.2026.01.032

Mitchell T. Bonney, Yu Zhang

Snow is an essential climate variable that is important for hydrology, climate, soil temperature and permafrost, vegetation, animal habitat, and socioeconomics. Wide-area snow cover dynamics (SCD), including the start and end of snow cover, are generally monitored by satellites with coarse spatial resolutions (250–1000 m) and high temporal (daily) resolutions. Higher spatial resolution (HSR) monitoring (10–30 m) has been limited to small areas because of computational constraints and infrequent cloud-free observations. Here, we develop a new method to map wide-area HSR SCD (snow start date, end date, length, periods, status) by leveraging the recently released Harmonized Landsat Sentinel-2 (HLS) v2.0, which has a 2–3-day revisit at 30-m resolution. The method is built around SpatialTemporal Asset Catalogs (STACs) and open-source Python tools. We utilize tiled datacubes, snow classification, and a model involving implausibility checking, cleaning, and finding peaks in data with gaps due to orbit frequencies and clouds. We demonstrate SCD mapping and validation across Canada’s Hudson Bay Lowlands (HBL) and an area in northern Alaska for each snow-year from 2018 to 2019 to 2023–2024 and multi-year composites (2018–2024). We also provide timing uncertainties and a quality metric for all pixels. Performance is best for snow end date, having strong relationships with both visually interpreted SCD from primarily very high-resolution imagery and measured local-scale snow depth. The combination of lower cloud cover and lower solar zenith angles during melt periods leads to lower uncertainties for snow end date compared to start date and length. Performance is better for all metrics at higher latitudes (e.g., northern Alaska), where satellite observations are more frequent due to increased orbit overlap. Although we have only completed validation for the HBL, Canada-wide products using this methodology are available publicly as STACs on the CCMEO Data Cube and will continue to be updated. Addition validation across Canada and methodology improvements are ongoing.

雪是一个重要的气候变量，对水文、气候、土壤温度和永久冻土、植被、动物栖息地和社会经济都很重要。广域积雪动态（SCD），包括积雪的开始和结束，通常由卫星监测，具有粗空间分辨率（250-1000 m）和高时间分辨率（日）。由于计算限制和不频繁的无云观测，高空间分辨率（HSR）监测（10-30米）仅限于小区域。在这里，我们利用最近发布的Harmonized Landsat Sentinel-2 (HLS) v2.0开发了一种新的方法来绘制广域高铁SCD（雪开始日期，结束日期，长度，周期，状态），该v2.0具有2 - 3天的30米分辨率重访。该方法是围绕时空资产目录（STACs）和开源Python工具构建的。我们使用了平铺数据库、雪分类和一个模型，该模型涉及不可信检查、清理和查找由于轨道频率和云而存在间隙的数据中的峰值。我们展示了加拿大哈德逊湾低地（HBL）和阿拉斯加北部地区的SCD制图和验证，包括2018年至2019年、2023年至2024年的每个雪年和多年合成（2018年至2024年）。我们还为所有像素提供了时间不确定性和质量度量。性能最好的是降雪结束日期，与主要是非常高分辨率图像的视觉解释SCD和测量的局地尺度雪深有很强的关系。融雪期较低的云量和较低的太阳天顶角相结合，导致雪结束日期的不确定性低于开始日期和长度。在高纬度地区（例如阿拉斯加北部），由于轨道重叠增加，卫星观测更加频繁，因此所有指标的性能都更好。虽然我们只完成了HBL的验证，但使用这种方法的加拿大范围内的产品在CCMEO数据立方上作为stac公开可用，并将继续更新。加拿大各地的附加验证和方法改进正在进行中。

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引用次数: 0

Set-CVGL: A new perspective on cross-view geo-localization with unordered ground-view image sets Set-CVGL：一种基于无序地视图像集的跨视点地理定位新视角

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-01-30 DOI: 10.1016/j.isprsjprs.2026.01.037

Qiong Wu , Panwang Xia , Lei Yu , Yi Liu , Mingtao Xiong , Liheng Zhong , Jingdong Chen , Ming Yang , Yongjun Zhang , Yi Wan

Cross-view geo-localization (CVGL) has been widely applied in fields such as robotic navigation and geographic information coupling. Existing approaches primarily use single images or fixed-view image sequences as queries, which limits perspective diversity. In contrast, when humans determine their location visually, they typically move around to gather multiple perspectives. This behavior suggests that integrating diverse visual cues can improve geo-localization reliability. Therefore, we propose a novel task: Cross-View Image Set Geo-Localization (Set-CVGL), which gathers multiple images with diverse perspectives as a query set for localization. To support this task, we introduce SetVL-480K, a benchmark comprising 480,000 ground images captured worldwide and their corresponding satellite images, with each satellite image corresponds to an average of 40 ground images from varied perspectives and locations. Furthermore, we propose FlexGeo, a flexible method designed for Set-CVGL that can also adapt to single-image and image-sequence inputs. FlexGeo includes two key modules: the Similarity-guided Feature Fuser (SFF), which adaptively fuses image features without prior content dependency, and the Individual-level Attributes Learner (IAL), leveraging geo-attributes of each image for comprehensive scene perception. FlexGeo consistently outperforms existing methods on SetVL-480K and four public datasets (VIGOR, University-1652, SeqGeo, and KITTI-CVL), achieving a 2.34

\times

improvement in localization accuracy on SetVL-480K. The codes and dataset will be available at https://github.com/Mabel0403/Set-CVGL.

交叉视角地理定位技术在机器人导航、地理信息耦合等领域有着广泛的应用。现有的方法主要使用单个图像或固定视图图像序列作为查询，这限制了视角的多样性。相比之下，当人类在视觉上确定自己的位置时，他们通常会四处走动，以收集多个视角。这种行为表明，整合不同的视觉线索可以提高地理定位的可靠性。因此，我们提出了一种新的任务：交叉视图图像集地理定位（Set- cvgl），该任务将具有不同视角的多幅图像作为定位的查询集。为了支持这项任务，我们引入了SetVL-480K，这是一个基准，包括48万张全球地面图像及其相应的卫星图像，每张卫星图像平均对应40张不同角度和位置的地面图像。此外，我们提出了FlexGeo，这是一种为Set-CVGL设计的灵活方法，也可以适应单图像和图像序列输入。FlexGeo包括两个关键模块：相似性引导的特征融合器（SFF），它自适应地融合图像特征，而不依赖于先前的内容；以及个人层面的属性学习器（IAL），利用每张图像的地理属性进行全面的场景感知。FlexGeo在SetVL-480K和四个公共数据集（VIGOR、University-1652、SeqGeo和KITTI-CVL）上的定位精度持续优于现有方法，在SetVL-480K上的定位精度提高了2.34倍。代码和数据集可在https://github.com/Mabel0403/Set-CVGL上获得。

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引用次数: 0

An advanced decoupled polarimetric calibration method for the LuTan-1 hybrid- and quadrature-polarimetric modes LuTan-1混合偏振模式和正交偏振模式的一种高级解耦偏振定标方法

IF 12.2 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

ISPRS Journal of Photogrammetry and Remote Sensing

Pub Date : 2026-01-30 DOI: 10.1016/j.isprsjprs.2026.01.035

Lizhi Liu, Lijie Huang, Yiding Wang, Pingping Lu, Bo Li, Liang Li, Robert Wang, Yirong Wu

During solar maximum, low-frequency spaceborne Polarimetric Synthetic Aperture Radar (PolSAR) systems suffer ionosphere-induced distortions that couple with system-induced polarimetric distortions. High-precision decoupled polarimetric calibration is therefore essential for obtaining high-fidelity PolSAR data. Existing point-target calibration methods lack a general approach for unbiased estimation of polarimetric distortion across multiple polarimetric modes and calibrator combinations, particularly under spatiotemporally varying ionospheric conditions. To address this, we derive the necessary conditions for unbiased estimation and propose a General Polarimetric Calibration Method (GPCM) applicable to various configurations. In addition, Enhanced Multi-Look Autofocus (EMLA), a modified STEC inversion method, is introduced for precise inversion of Slant Total Electron Content (STEC), enabling estimation of the spatiotemporally varying Faraday rotation angle for system distortion decoupling and PolSAR data compensation. GPCM applied to LuTan-1 HP and QP data results in HH/VV amplitude and phase imbalances of 0.0433 dB (STD: 0.017) and − 0.60° (STD: 1.02°), respectively, measured on trihedral corner reflectors. Calibration results also indicate that QP mode isolation exceeds 39 dB, while estimated axial ratios for HP mode are lower than 0.115 dB. Under comparable conditions, the results of GPCM are consistent with the Freeman analytical method. Furthermore, EMLA outperforms existing STEC inversion methods (COA, MLA, and GIM-based mapping), achieving a mean absolute difference of 1.95 TECU compared with in-situ measurements while demonstrating applicability to general scenes. Overall, the effectiveness of GPCM and EMLA in the LuTan-1 calibration mission is confirmed, indicating their potential for future PolSAR calibration tasks. The primary calibrated experimental dataset is publicly available at https://radars.ac.cn/web/data/getData?dataType=HPSAREADEN&pageType=en.

在太阳活动极大期，低频星载极化合成孔径雷达（PolSAR）系统遭受电离层诱导的畸变，这种畸变与系统诱导的极化畸变耦合。因此，高精度解耦极化校准对于获得高保真的PolSAR数据至关重要。现有的点目标校准方法缺乏一种通用的方法来无偏估计跨多个极化模式和校准器组合的极化失真，特别是在时空变化的电离层条件下。为了解决这个问题，我们推导了无偏估计的必要条件，并提出了一种适用于各种配置的通用偏振校准方法（GPCM）。此外，提出了一种改进的STEC反演方法——Enhanced Multi-Look Autofocus (EMLA)，用于精确反演倾斜总电子含量（STEC），从而估算出法拉第旋转角的时空变化，从而实现系统畸变解耦和PolSAR数据补偿。采用GPCM对鲁坦1号的HP和QP数据进行处理，在三面角反射镜上测得的HH/VV振幅和相位不平衡分别为0.0433 dB （STD: 0.017）和- 0.60°（STD: 1.02°）。校准结果还表明，QP模式隔离度超过39 dB，而HP模式的估计轴向比低于0.115 dB。在可比条件下，GPCM的计算结果与Freeman分析方法一致。此外，EMLA优于现有的STEC反演方法（基于COA、MLA和基于gimm的制图），与原位测量相比，平均绝对差为1.95 TECU，同时证明了对一般场景的适用性。总体而言，GPCM和EMLA在LuTan-1校准任务中的有效性得到了证实，表明它们在未来PolSAR校准任务中的潜力。主要校准的实验数据集可在https://radars.ac.cn/web/data/getData?dataType=HPSAREADEN&pageType=en上公开获取。

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