Pub Date : 2026-02-02DOI: 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
{"title":"A novel transformer-based CO2 retrieval framework incorporating prior constraint and hierarchical features injection: assessment of transferability for Tansat-2","authors":"Lingfeng Zhang, Lu Zhang, Xingying Zhang, Tiantao Cheng, Xifeng Cao, Tongwen Li, Dongdong Liu, Yang Zhang, Yuhan Jiang, Ruohua Hu, Haiyang Dou, Lin Chen","doi":"10.1016/j.isprsjprs.2026.01.039","DOIUrl":"https://doi.org/10.1016/j.isprsjprs.2026.01.039","url":null,"abstract":"","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"23 1","pages":""},"PeriodicalIF":12.7,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.isprsjprs.2026.01.040
Hesham M. El-Asmar, Mahmoud Sh. Felfla
{"title":"Comparative assessment of AI-based and classical DSAS approaches in multi-temporal shoreline prediction: A case study of Ras El-Bar coast, Egypt","authors":"Hesham M. El-Asmar, Mahmoud Sh. Felfla","doi":"10.1016/j.isprsjprs.2026.01.040","DOIUrl":"https://doi.org/10.1016/j.isprsjprs.2026.01.040","url":null,"abstract":"","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"38 1","pages":""},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 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.
{"title":"Multi-object tracking of vehicles and anomalous states in remote sensing videos: Joint learning of historical trajectory guidance and ID prediction","authors":"Bin Wang , Yuan Zhou , Haigang Sui , Guorui Ma , Peng Cheng , Di Wang","doi":"10.1016/j.isprsjprs.2026.01.038","DOIUrl":"10.1016/j.isprsjprs.2026.01.038","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 383-406"},"PeriodicalIF":12.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 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.
{"title":"A geometric Cross-Propagation-Calibration method for SAR constellation based on the graph theory","authors":"Yitong Luo , Xiaolan Qiu , Bei Lin , Zekun Jiao , Wei Wang , Chibiao Ding","doi":"10.1016/j.isprsjprs.2026.01.007","DOIUrl":"10.1016/j.isprsjprs.2026.01.007","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 346-359"},"PeriodicalIF":12.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 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.
{"title":"Monitoring snow cover dynamics at 30-m resolution in higher latitude regions using Harmonized Landsat Sentinel-2","authors":"Mitchell T. Bonney, Yu Zhang","doi":"10.1016/j.isprsjprs.2026.01.032","DOIUrl":"10.1016/j.isprsjprs.2026.01.032","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 360-382"},"PeriodicalIF":12.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 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 improvement in localization accuracy on SetVL-480K. The codes and dataset will be available at https://github.com/Mabel0403/Set-CVGL.
{"title":"Set-CVGL: A new perspective on cross-view geo-localization with unordered ground-view image sets","authors":"Qiong Wu , Panwang Xia , Lei Yu , Yi Liu , Mingtao Xiong , Liheng Zhong , Jingdong Chen , Ming Yang , Yongjun Zhang , Yi Wan","doi":"10.1016/j.isprsjprs.2026.01.037","DOIUrl":"10.1016/j.isprsjprs.2026.01.037","url":null,"abstract":"<div><div>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 <strong>a query set</strong> 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<span><math><mo>×</mo></math></span> improvement in localization accuracy on SetVL-480K. The codes and dataset will be available at <span><span>https://github.com/Mabel0403/Set-CVGL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 328-345"},"PeriodicalIF":12.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 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.
{"title":"An advanced decoupled polarimetric calibration method for the LuTan-1 hybrid- and quadrature-polarimetric modes","authors":"Lizhi Liu, Lijie Huang, Yiding Wang, Pingping Lu, Bo Li, Liang Li, Robert Wang, Yirong Wu","doi":"10.1016/j.isprsjprs.2026.01.035","DOIUrl":"10.1016/j.isprsjprs.2026.01.035","url":null,"abstract":"<div><div>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 <span><span>https://radars.ac.cn/web/data/getData?dataType=HPSAREADEN&pageType=en</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 310-327"},"PeriodicalIF":12.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.isprsjprs.2026.01.034
Xiaochen Yang , Haiping Wang , Yuan Liu , Bisheng Yang , Zhen Dong
We propose RegScorer, a model learning to identify the optimal transformation to register unaligned point clouds. Existing registration advancements can generate a set of candidate transformations, which are then evaluated using conventional metrics such as Inlier Ratio (IR), Mean Squared Error (MSE) or Chamfer Distance (CD). The candidate achieving the best score is selected as the final result. However, we argue that these metrics often fail to select the correct transformation, especially in challenging scenarios involving symmetric objects, repetitive structures, or low-overlap regions. This leads to significant degradation in registration performance, a problem that has long been overlooked. The core issue lies in their limited focus on local geometric consistency and inability to capture two key conflict cases of misalignment: (1) point pairs that are spatially close after alignment but have conflicting features, and (2) point pairs with high feature similarity but large spatial distances after alignment. To address this, we propose RegScorer, which models both the spatial and feature relationships of all point pairs. This allows RegScorer to learn to capture the above conflict cases and provides a more reliable score for transformation quality. On the 3DLoMatch and ScanNet datasets, RegScorer demonstrate 19.3% and 14.1% improvements in registration recall, leading to 4.7% and 5.1% accuracy gains in multiview registration. Moreover, when generalized to symmetric and low-texture outdoor scenes, RegScorer achieves a 25% increase in transformation recall over IR metric, highlighting its robustness and generalizability. The pre-trained model and the complete code repository can be accessed at https://github.com/WHU-USI3DV/RegScorer.
我们提出了RegScorer，一个模型学习来识别最优的转换，以配准不对齐的点云。现有的配准进展可以生成一组候选变换，然后使用传统的指标（如Inlier Ratio （IR）、均方误差（MSE）或倒角距离（CD））对其进行评估。成绩最好的候选人被选为最终成绩。然而，我们认为这些指标经常不能选择正确的转换，特别是在涉及对称对象、重复结构或低重叠区域的具有挑战性的场景中。这将导致注册性能的显著下降，这是一个长期被忽视的问题。核心问题在于它们对局部几何一致性的关注有限，无法捕捉到两种关键的不对齐冲突情况：(1)对齐后空间接近但特征冲突的点对；(2)对齐后特征相似度高但空间距离大的点对。为了解决这个问题，我们提出了RegScorer，它对所有点对的空间和特征关系进行建模。这允许RegScorer学习捕获上述冲突案例，并为转换质量提供更可靠的评分。在3DLoMatch和ScanNet数据集上，RegScorer的注册召回率分别提高了19.3%和14.1%，导致多视图注册的准确率分别提高了4.7%和5.1%。此外，当推广到对称和低纹理户外场景时，RegScorer的变换召回率比IR指标提高了25%，突出了其鲁棒性和泛化性。预训练的模型和完整的代码存储库可以在https://github.com/WHU-USI3DV/RegScorer上访问。
{"title":"RegScorer: Learning to select the best transformation of point cloud registration","authors":"Xiaochen Yang , Haiping Wang , Yuan Liu , Bisheng Yang , Zhen Dong","doi":"10.1016/j.isprsjprs.2026.01.034","DOIUrl":"10.1016/j.isprsjprs.2026.01.034","url":null,"abstract":"<div><div>We propose RegScorer, a model learning to identify the optimal transformation to register unaligned point clouds. Existing registration advancements can generate a set of candidate transformations, which are then evaluated using conventional metrics such as Inlier Ratio (IR), Mean Squared Error (MSE) or Chamfer Distance (CD). The candidate achieving the best score is selected as the final result. However, we argue that these metrics often fail to select the correct transformation, especially in challenging scenarios involving symmetric objects, repetitive structures, or low-overlap regions. This leads to significant degradation in registration performance, a problem that has long been overlooked. The core issue lies in their limited focus on local geometric consistency and inability to capture two key conflict cases of misalignment: (1) point pairs that are spatially close after alignment but have conflicting features, and (2) point pairs with high feature similarity but large spatial distances after alignment. To address this, we propose RegScorer, which models both the spatial and feature relationships of all point pairs. This allows RegScorer to learn to capture the above conflict cases and provides a more reliable score for transformation quality. On the 3DLoMatch and ScanNet datasets, RegScorer demonstrate <strong>19.3</strong>% and <strong>14.1</strong>% improvements in registration recall, leading to <strong>4.7</strong>% and <strong>5.1</strong>% accuracy gains in multiview registration. Moreover, when generalized to symmetric and low-texture outdoor scenes, RegScorer achieves a <strong>25</strong>% increase in transformation recall over IR metric, highlighting its robustness and generalizability. The pre-trained model and the complete code repository can be accessed at <span><span>https://github.com/WHU-USI3DV/RegScorer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 266-277"},"PeriodicalIF":12.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.isprsjprs.2026.01.031
Josef Taher , Eric Hyyppä , Matti Hyyppä , Klaara Salolahti , Xiaowei Yu , Leena Matikainen , Antero Kukko , Matti Lehtomäki , Harri Kaartinen , Sopitta Thurachen , Paula Litkey , Ville Luoma , Markus Holopainen , Gefei Kong , Hongchao Fan , Petri Rönnholm , Matti Vaaja , Antti Polvivaara , Samuli Junttila , Mikko Vastaranta , Juha Hyyppä
<div><div>Climate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing, but challenges remain in leveraging deep learning techniques and identifying rare tree species in class-imbalanced datasets. This study addresses these gaps by conducting a comprehensive benchmark of deep learning and traditional shallow machine learning methods for tree species classification. For the study, we collected high-density multispectral ALS data (<span><math><mrow><mo>></mo><mn>1000</mn></mrow></math></span> <span><math><mrow><mi>pts</mi><mo>/</mo><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>) at three wavelengths using the FGI-developed HeliALS system, complemented by existing Optech Titan data (35 <span><math><mrow><mi>pts</mi><mo>/</mo><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>), to evaluate the species classification accuracy of various algorithms in a peri-urban study area located in southern Finland. We established a field reference dataset of 6326 segments across nine species using a newly developed browser-based crowdsourcing tool, which facilitated efficient data annotation. The ALS data, including a training dataset of 1065 segments, was shared with the scientific community to foster collaborative research and diverse algorithmic contributions. Based on 5261 test segments, our findings demonstrate that point-based deep learning methods, particularly a point transformer model, outperformed traditional machine learning and image-based deep learning approaches on high-density multispectral point clouds. For the high-density ALS dataset, a point transformer model provided the best performance reaching an overall (macro-average) accuracy of 87.9% (74.5%) with a training set of 1065 segments and 92.0% (85.1%) with a larger training set of 5000 segments. With 1065 training segments, the best image-based deep learning method, DetailView, reached an overall (macro-average) accuracy of 84.3% (63.9%), whereas a shallow random forest (RF) classifier achieved an overall (macro-average) accuracy of 83.2% (61.3%). For the sparser ALS dataset, an RF model topped the list with an overall (macro-average) accuracy of 79.9% (57.6%), closely followed by the point transformer at 79.6% (56.0%). Importantly, the overall classification accuracy of the point transformer model on the HeliALS data increased from 73.0% with no spectral information to 84.7% with single-channel reflectance, and to 87.9% with spectral information of all the three channels. Furthermore, we studied the scaling of the classification accuracy as a function of point density and training set size using 5-fold cross-validation of our dataset. Based on our findings, multispectral information is especially beneficial for sparse point clouds with 1–50 <span><math>
{"title":"Multispectral airborne laser scanning for tree species classification: A benchmark of machine learning and deep learning algorithms","authors":"Josef Taher , Eric Hyyppä , Matti Hyyppä , Klaara Salolahti , Xiaowei Yu , Leena Matikainen , Antero Kukko , Matti Lehtomäki , Harri Kaartinen , Sopitta Thurachen , Paula Litkey , Ville Luoma , Markus Holopainen , Gefei Kong , Hongchao Fan , Petri Rönnholm , Matti Vaaja , Antti Polvivaara , Samuli Junttila , Mikko Vastaranta , Juha Hyyppä","doi":"10.1016/j.isprsjprs.2026.01.031","DOIUrl":"10.1016/j.isprsjprs.2026.01.031","url":null,"abstract":"<div><div>Climate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing, but challenges remain in leveraging deep learning techniques and identifying rare tree species in class-imbalanced datasets. This study addresses these gaps by conducting a comprehensive benchmark of deep learning and traditional shallow machine learning methods for tree species classification. For the study, we collected high-density multispectral ALS data (<span><math><mrow><mo>></mo><mn>1000</mn></mrow></math></span> <span><math><mrow><mi>pts</mi><mo>/</mo><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>) at three wavelengths using the FGI-developed HeliALS system, complemented by existing Optech Titan data (35 <span><math><mrow><mi>pts</mi><mo>/</mo><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>), to evaluate the species classification accuracy of various algorithms in a peri-urban study area located in southern Finland. We established a field reference dataset of 6326 segments across nine species using a newly developed browser-based crowdsourcing tool, which facilitated efficient data annotation. The ALS data, including a training dataset of 1065 segments, was shared with the scientific community to foster collaborative research and diverse algorithmic contributions. Based on 5261 test segments, our findings demonstrate that point-based deep learning methods, particularly a point transformer model, outperformed traditional machine learning and image-based deep learning approaches on high-density multispectral point clouds. For the high-density ALS dataset, a point transformer model provided the best performance reaching an overall (macro-average) accuracy of 87.9% (74.5%) with a training set of 1065 segments and 92.0% (85.1%) with a larger training set of 5000 segments. With 1065 training segments, the best image-based deep learning method, DetailView, reached an overall (macro-average) accuracy of 84.3% (63.9%), whereas a shallow random forest (RF) classifier achieved an overall (macro-average) accuracy of 83.2% (61.3%). For the sparser ALS dataset, an RF model topped the list with an overall (macro-average) accuracy of 79.9% (57.6%), closely followed by the point transformer at 79.6% (56.0%). Importantly, the overall classification accuracy of the point transformer model on the HeliALS data increased from 73.0% with no spectral information to 84.7% with single-channel reflectance, and to 87.9% with spectral information of all the three channels. Furthermore, we studied the scaling of the classification accuracy as a function of point density and training set size using 5-fold cross-validation of our dataset. Based on our findings, multispectral information is especially beneficial for sparse point clouds with 1–50 <span><math>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 278-309"},"PeriodicalIF":12.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.isprsjprs.2026.01.018
Seyed Babak Haji Seyed Asadollah, Giorgos Mountrakis, Stephen B. Shaw
The accelerating frequency, duration and intensity of extreme heat events demand accurate, spatially complete heat exposure metrics. Here, a modeling approach is presented for estimating the daily-maximum Heat Index (HI) at 1 km spatial resolution. Our study area covered the conterminous United States (CONUS) during the warm season (May to September) between 2003 and 2023. More than 4.6 million observations from approximately 2000 weather stations were paired with weather-related, geographical, land cover and historical climatic factors to develop the proposed Satellite-based Heat Index estimatioN modEl (SHINE). Selected explanatory variables at daily temporal intervals included reanalysis products from Modern-Era Retrospective analysis for Research and Applications (MERRA) and direct satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor.
The most influential variables for HI estimation were the MERRA surface layer height and specific humidity products and the dual-pass MODIS daily land surface temperatures. These were followed by land cover products capturing water and forest presence, historical norms of wind speed and maximum temperature, elevation information and the corresponding day of year. An Extreme Gradient Boosting (XGBoost) regressor trained with spatial cross-validation explained 93 % of the variance (R2 = 0.93) and attained a Root Mean Square Error (RMSE) of 1.9°C and a Mean Absolute Error (MAE) of 1.4°C. Comparison of alternative configurations showed that while a MERRA-only model provided slightly higher accuracy (RMSE of 1.8°C), its coarse resolution failed to capture fine-scale heat variations. Conversely, a MODIS-only model offered kilometer-scale spatial resolution but with higher estimation errors (RMSE of 2.9°C). Integrating both MERRA and MODIS sources enabled SHINE to maintain spatial detail and preserved accuracy, underscoring the complementary strengths of reanalysis and satellite products. SHINE also demonstrated resistance to missing MODIS LST observations due to clouds as the additional RMSE error was approximately 0.5°C in the worst case of missing both morning and afternoon MODIS land surface temperature observations. Spatial error analysis revealed <1.7°C RMSE in arid and Mediterranean zones but larger, more heterogeneous errors in the humid Midwest and High Plains. From the policy perspective and considering the HI operational range for public-health heat effects, the proposed SHINE approach outperformed typically used proxies, such as land surface and air temperature. The resulting 1 km daily HI estimations can potentially be used as the foundation of the first wall-to-wall, multi-decadal, high resolution heat dataset for CONUS and offer actionable information for public-health heat studies, energy-demand forecasting and environmental-justice implications.
{"title":"Satellite-based heat Index estimatioN modEl (SHINE): An integrated machine learning approach for the conterminous United States","authors":"Seyed Babak Haji Seyed Asadollah, Giorgos Mountrakis, Stephen B. Shaw","doi":"10.1016/j.isprsjprs.2026.01.018","DOIUrl":"10.1016/j.isprsjprs.2026.01.018","url":null,"abstract":"<div><div>The accelerating frequency, duration and intensity of extreme heat events demand accurate, spatially complete heat exposure metrics. Here, a modeling approach is presented for estimating the daily-maximum Heat Index (HI) at 1 km spatial resolution. Our study area covered the conterminous United States (CONUS) during the warm season (May to September) between 2003 and 2023. More than 4.6 million observations from approximately 2000 weather stations were paired with weather-related, geographical, land cover and historical climatic factors to develop the proposed Satellite-based Heat Index estimatioN modEl (SHINE). Selected explanatory variables at daily temporal intervals included reanalysis products from Modern-Era Retrospective analysis for Research and Applications (MERRA) and direct satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor.</div><div>The most influential variables for HI estimation were the MERRA surface layer height and specific humidity products and the dual-pass MODIS daily land surface temperatures. These were followed by land cover products capturing water and forest presence, historical norms of wind speed and maximum temperature, elevation information and the corresponding day of year. An Extreme Gradient Boosting (XGBoost) regressor trained with spatial cross-validation explained 93 % of the variance (R<sup>2</sup> = 0.93) and attained a Root Mean Square Error (RMSE) of 1.9°C and a Mean Absolute Error (MAE) of 1.4°C. Comparison of alternative configurations showed that while a MERRA-only model provided slightly higher accuracy (RMSE of 1.8°C), its coarse resolution failed to capture fine-scale heat variations. Conversely, a MODIS-only model offered kilometer-scale spatial resolution but with higher estimation errors (RMSE of 2.9°C). Integrating both MERRA and MODIS sources enabled SHINE to maintain spatial detail and preserved accuracy, underscoring the complementary strengths of reanalysis and satellite products. SHINE also demonstrated resistance to missing MODIS LST observations due to clouds as the additional RMSE error was approximately 0.5°C in the worst case of missing both morning and afternoon MODIS land surface temperature observations. Spatial error analysis revealed <1.7°C RMSE in arid and Mediterranean zones but larger, more heterogeneous errors in the humid Midwest and High Plains. From the policy perspective and considering the HI operational range for public-health heat effects, the proposed SHINE approach outperformed typically used proxies, such as land surface and air temperature. The resulting 1 km daily HI estimations can potentially be used as the foundation of the first wall-to-wall, multi-decadal, high resolution heat dataset for CONUS and offer actionable information for public-health heat studies, energy-demand forecasting and environmental-justice implications.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 209-230"},"PeriodicalIF":12.2,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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