ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Advancing table beet root yield estimation via unmanned aerial systems (UAS) multi-modal sensing","authors":"Mohammad S. Saif ,&nbsp;Robert Chancia ,&nbsp;Sean P. Murphy ,&nbsp;Sarah Pethybridge ,&nbsp;Jan van Aardt","doi":"10.1016/j.isprsjprs.2025.12.026","DOIUrl":"10.1016/j.isprsjprs.2025.12.026","url":null,"abstract":"<div><div>Unmanned aerial systems (UAS) offer significant potential to improve agricultural practices due to their multi-modal payload capacity, ease of deployment, and lower cost. However, there is a need to expand UAS capabilities by including root crops, offering robust, growth-stage-independent models, and providing a comprehensive assessment of various imaging systems, i.e., identifying application-specific sensing modalities. This study aims to tackle those challenges by presenting a unified Gaussian Process Regression (GPR) model for predicting end-of-season table beet (a subterranean root crop) yield using UAS-derived spectral and structural features, combined with meteorological data, while remaining robust to flight and harvest timing. Field trials were conducted at Cornell AgriTech in Geneva, NY during the 2021 and 2022 growing seasons. UAS flights captured five-band (475, 560, 668, 717, and 840 nm) multispectral imagery, hyperspectral imagery (400–1000 nm), and light detection and ranging (LiDAR) data at multiple times throughout the season. Our model achieved <em>R²_test</em> = 0.81 and MAPE_test = 15.7 % using only multispectral imagery, while the hyperspectral + LiDAR model attained <em>R²_test</em> = 0.79 and MAPE_test = 17.4 %, which is comparable to recent root yield modeling studies using UAS data. Shapely analysis was performed to gain further insight into model behavior. This analysis revealed canopy volume information to contain high relative importance, as compared to other features, for table beet root yield estimation. Our study demonstrated that UAS-based imaging, combined with a unified machine learning model, can effectively predict root crop yield, providing a scalable and transferable approach for precision agriculture.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 542-560"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925600","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}

{"title":"Roof-aware indoor BIM reconstruction from LiDAR via graph-attention for residential buildings","authors":"Biao Xiong ,&nbsp;Bohan Wang ,&nbsp;Yiyi Liu ,&nbsp;Liangliang Wang ,&nbsp;Yanchao Yang ,&nbsp;Liang Zhou ,&nbsp;Qiegen Liu","doi":"10.1016/j.isprsjprs.2025.12.024","DOIUrl":"10.1016/j.isprsjprs.2025.12.024","url":null,"abstract":"<div><div>Building Information Models (BIMs) provide structured, parametric representations that are fundamental for simulation, facility management, and digital twin applications. However, reconstructing BIMs from terrestrial LiDAR scans remains challenging due to clutter, occlusions, and the geometric complexity of roof structures. This paper presents a <strong>roof-aware scan-to-BIM pipeline</strong> tailored for residential buildings, which processes indoor LiDAR data through four geometric abstractions, raw points, superpoints, triangle meshes, and volumetric polyhedra, each represented by task-specific graphs. The pipeline integrates three modules: <strong>LGNet</strong> for semantic segmentation, <strong>QTNet</strong> for floor plan reconstruction, and <strong>PPO</strong> for roof–floor fusion. It demonstrates strong cross-dataset generalization, being trained on Structured3D and fine-tuned on the real-world WHUTS dataset. The method produces watertight, Revit-compatible BIMs with an average surface deviation of <strong>9 mm RMS</strong> on WHUTS scenes featuring slanted roofs. Compared with state-of-the-art scan-to-BIM and floor plan reconstruction methods, the proposed approach achieves higher geometric accuracy on scenes with slanted roofs, reducing surface reconstruction error by over <strong>12–18%</strong> and improving layout reconstruction F1-scores by up to <strong>6–8%</strong>. The proposed framework provides a robust, accurate, and fully automated solution for roof-aware BIM reconstruction of residential buildings from terrestrial LiDAR data, offering comprehensive support for slanted roof modeling. The source code and datasets are publicly available at <span><span>https://github.com/Wangbohan-x/roof-aware-scan2bim.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 408-420"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884003","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}

{"title":"A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data","authors":"Hyunho Lee,&nbsp;Wenwen Li","doi":"10.1016/j.isprsjprs.2025.12.023","DOIUrl":"10.1016/j.isprsjprs.2025.12.023","url":null,"abstract":"<div><div>Mapping water extent during a flood event is essential for effective disaster management throughout all phases: mitigation, preparedness, response, and recovery. In particular, during the response stage, when timely and accurate information is important, Synthetic Aperture Radar (SAR) data are primarily employed to produce water extent maps. This is because SAR sensors can observe through cloud cover and operate both day and night, whereas Multispectral Imaging (MSI) data, despite providing higher mapping accuracy, are only available under cloud-free and daytime conditions. Recently, leveraging the complementary characteristics of SAR and MSI data through a multimodal approach has emerged as a promising strategy for advancing water extent mapping using deep learning models. This approach is particularly beneficial when timely post-flood observations, acquired during or shortly after the flood peak, are limited, as it enables the use of all available imagery for more accurate post-flood water extent mapping. However, the adaptive integration of partially available MSI data into the SAR-based post-flood water extent mapping process remains underexplored. To bridge this research gap, we propose the Spatially Masked Adaptive Gated Network (SMAGNet), a multimodal deep learning model that utilizes SAR data as the primary input for post-flood water extent mapping and integrates complementary MSI data through feature fusion. In experiments on the C2S-MS Floods dataset, SMAGNet consistently outperformed other multimodal deep learning models in prediction performance across varying levels of MSI data availability. Specifically, SMAGNet achieved the highest IoU score of 86.47% using SAR and MSI data and maintained the highest performance with an IoU score of 79.53% even when MSI data were entirely missing. Furthermore, we found that even when MSI data were completely missing, the performance of SMAGNet remained statistically comparable to that of a U-Net model trained solely on SAR data. These findings indicate that SMAGNet enhances the model robustness to missing data as well as the applicability of multimodal deep learning in real-world flood management scenarios. The source code is available at <span><span>https://github.com/ASUcicilab/SMAGNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 492-508"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902910","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}

{"title":"Beyond synthetic scenarios: Weakly-supervised super-resolution for spatiotemporally misaligned remote sensing images","authors":"Quanyi Guo ,&nbsp;Rui Liu ,&nbsp;Yangtian Fang ,&nbsp;Yi Gao ,&nbsp;Jun Chen ,&nbsp;Xin Tian","doi":"10.1016/j.isprsjprs.2025.12.019","DOIUrl":"10.1016/j.isprsjprs.2025.12.019","url":null,"abstract":"<div><div>Deep learning-based remote sensing image super-resolution is crucial for enhancing the spatial resolution of Earth observation data. Due to the absence of perfectly aligned pairs of high- and low-resolution remote sensing images, most existing supervised and self-supervised approaches rely on synthetic degradation models or internal structural consistency to generate training data. Consequently, these methods suffer from the domain gap between synthetic and real datasets, which limits their ability to model realistic degradation and degrades their performance in real scenes. To overcome this challenge, we propose STANet, a weakly-supervised super-resolution method for spatiotemporally misaligned remote sensing images. In particular, STANet directly utilizes images of the same region captured by multiple satellites at different resolutions as datasets, to boost the real remote sensing image super-resolution performance. However, this approach also introduces new challenges related to spatiotemporal misalignment. To address this, we design a spatiotemporal align module that includes a Scale Align Module (SAM) and a Temporal Align Module (TAM). SAM uses affine transformations to align spatial features at both the pixel and global levels, while TAM applies window-based attention to adjust the weight of image content, mitigating the misleading effects of temporal misalignment on results. Besides, we also design a style encoder based on contrastive learning and a structure encoder based on variational inference, which guide SAM and TAM for feature alignment and enhance adaptability. Finally, the feature-aligned output, after upsampling, are fused with the high-frequency-enhancing output of the texture transfer module through the weighted fusion module to generate the super-resolution image. Extensive experiments on synthetic datasets based on AID and RSSR25, real datasets captured by GaoFen satellites, and cross-satellite experiments on Landsat-8 datasets demonstrate STANet’s superiority over other state-of-the-art methods.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 524-541"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902921","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}

{"title":"Mapping oil spills under varying sun glint conditions using a diffusion model with spatial-spectral-frequency constraints","authors":"Kai Du ,&nbsp;Yi Ma ,&nbsp;Zhongwei Li ,&nbsp;Rongjie Liu ,&nbsp;Zongchen Jiang ,&nbsp;Junfang Yang","doi":"10.1016/j.isprsjprs.2025.11.032","DOIUrl":"10.1016/j.isprsjprs.2025.11.032","url":null,"abstract":"<div><div>Marine oil spill detection via optical remote sensing is critically challenged by complex optical signatures induced by sun glint, which causes contrast reversals and masks the spectral features of different oil emulsion types. This study introduces a novel deep learning framework, the Spatial-Spectral Attention Conditioned Diffusion Probabilistic Model with Dual-branch Frequency Parser (OSS-Diff). The core architecture integrates two specialized modules. The first, a physics-informed Dual-branch Frequency Parser (DF-P) module, is designed to disentangle contrast variations by separately processing high-frequency edge features (characteristic of bright spills in strong glint) and low-frequency background information (typical of dark spills in weak glint). Concurrently, a systematically designed Spatial-Spectral Attention (SS-A) module targets spectral ambiguity by adaptively amplifying the discriminative features crucial for identifying oil emulsification states. Extensive experiments confirm the model’s superior performance. Under weak sun glint, it achieved F1-scores of 0.907 for non-emulsified and 0.862 for emulsified slicks. For positive-contrast spills under strong sun glint, it achieved an F1-score of 0.918. Ablation studies validate the synergistic effect of the proposed components, with the full model achieving a 4.9% F1-score gain over the baseline. Significantly, this work also reveals a potential link between the contrast inversion angle and the oil’s refractive index, suggesting a novel avenue for remotely characterizing the physical properties of oil spills. This research provides a robust framework for automated oil spill monitoring, offering a reliable model for environmental protection and emergency response.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 48-61"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657554","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}

{"title":"Cross-satellite hierarchical multimodal denoising for hyperspectral imagery","authors":"Minghua Wang ,&nbsp;Bo Shang ,&nbsp;Yilun Li ,&nbsp;Xin Zhao ,&nbsp;Lianru Gao ,&nbsp;Xu Sun ,&nbsp;Longfei Ren","doi":"10.1016/j.isprsjprs.2025.11.028","DOIUrl":"10.1016/j.isprsjprs.2025.11.028","url":null,"abstract":"<div><div>The rapid proliferation of remote sensing satellite constellations has ushered in an era of unprecedented access to hyperspectral (HS) and multispectral (MS) Earth observation data. HS denoising remains a critical research focus for enhancing the interpretation and application of satellite data. However, on the one hand, the available characteristics of noisy HS satellite data are often limited under real-world and unknown conditions. On the other hand, existing works ignore the complementary advantages offered by MS satellite imagery that can be employed to enhance HS denoising performance. Leveraging the synergistic potential of HS and MS data, this study pioneers a novel paradigm for the joint exploitation and optimization of multi-source remote sensing satellites. A novel Hierarchical Dual Tucker Decomposition (DTucker) framework is proposed to capitalize on the low-rank (LR) tensor property of HS and MS cross-satellite data. The inheritance of properties from the original tensor to the core tensor is explored through a manually designed model-driven constraint or a data-driven multilayer perceptron (MLP) framework. This enables robust integration of MS-derived spatial richness with HS details and significantly enhances the denoising capability. We construct HS–MS data pairs from real satellite observations, including Earth Observing-1, Sentinel-2, Gaofen-1, Gaofen-5, and Gaofen-6. Four datasets span diverse scenes such as runways, urban areas, rivers, and farmlands. The proposed method demonstrates strong generalization across various satellite combinations and application scenarios. Notably, the incorporation of MS data markedly enhances both class discrimination and structural detail in the denoised HS outputs, promoting the performance of subsequent classification and analysis tasks. The datasets and codes implemented in MATLAB and PYTHON will be available on the website: <span><span>https://github.com/MinghuaWang123/DTucker</span><svg><path></path></svg></span>, contributing to the remote sensing community.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 94-108"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684885","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}

{"title":"Extrapolate azimuth angles: Text and edge guided ISAR image generation based on foundation model","authors":"Jiawei Zhang,&nbsp;Xiaolin Zhou,&nbsp;Weidong Jiang,&nbsp;Xiaolong Su,&nbsp;Zhen Liu,&nbsp;Li Liu","doi":"10.1016/j.isprsjprs.2025.12.002","DOIUrl":"10.1016/j.isprsjprs.2025.12.002","url":null,"abstract":"<div><div>Inverse Synthetic Aperture Radar (ISAR) has been widely applied in remote sensing and space target monitoring. Automatic Target Recognition (ATR) based on ISAR imagery plays a critical role in target interpretation and pose estimation. With the growing adoption of intelligent methods in the ATR domain, the quantity and quality of ISAR data have become decisive factors influencing algorithm performance. However, due to the complexity of ISAR imaging algorithms and the high cost of data acquisition, high-quality ISAR image datasets remain extremely scarce. As a result, learning the underlying characteristics of existing ISAR data to generate large-scale usable samples has become a pressing research focus. Although some preliminary studies have explored ISAR image data augmentation, most of them rely on image sequence interpolation or conditional generation, both of which exhibit critical limitations: the former requires densely sampled image sequences with small angular intervals, while the latter can only model the mapping between limited azimuth conditions and ISAR images. Neither approach is capable of generating images of new targets under unseen azimuth conditions, resulting in poor generalization and leaving substantial room for further exploration. To address these limitations, we formally define a novel research problem, termed ISAR azimuth angle extrapolation. This task fundamentally involves high-dimensional, structured, cross-view image synthesis, requiring the restoration of visual details while ensuring physical consistency and structural stability. To address this problem, we propose ISAR-ExtraNet, a foundation-model-based framework for ISAR azimuth angle extrapolation. ISAR-ExtraNet leverages the strong representation, modeling, and generalization capabilities of pretrained foundation models to generate ISAR images of new targets under novel azimuth conditions. Specifically, the model employs a two-stage coarse-to-fine fine-tuning strategy, incorporating optical image contours and scattering center distribution constraints to guide the generation process. This design enhances both semantic alignment and structural fidelity in the generated ISAR images. Comprehensive experiments demonstrate that ISAR-ExtraNet significantly outperforms baseline methods and fine-tuned foundation models, achieving 28.76 dB in PSNR and 0.80 in SSIM. We hope that the training paradigm introduced in ISAR-ExtraNet will inspire further exploration of the ISAR azimuth extrapolation problem and foster progress in this emerging research area.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 109-123"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684886","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}

{"title":"CityVLM: Towards sustainable urban development via multi-view coordinated vision–language model","authors":"Junjue Wang ,&nbsp;Weihao Xuan ,&nbsp;Heli Qi ,&nbsp;Zihang Chen ,&nbsp;Hongruixuan Chen ,&nbsp;Zhuo Zheng ,&nbsp;Junshi Xia ,&nbsp;Yanfei Zhong ,&nbsp;Naoto Yokoya","doi":"10.1016/j.isprsjprs.2025.11.030","DOIUrl":"10.1016/j.isprsjprs.2025.11.030","url":null,"abstract":"<div><div>Vision–language models (VLMs) have shown remarkable promise in Earth Vision, particularly in providing human-interpretable analysis of remote sensing imagery. While existing VLMs excel at general visual perception tasks, they often fall short in addressing the complex needs of geoscience, which requires comprehensive urban analysis across geographical, social, and economic dimensions. To bridge this gap, we expand VLM capabilities to tackle sustainable urban development challenges by integrating two complementary sources: remote sensing (RS) and street-view (SV) imagery. Specifically, we first design a multi-view vision–language dataset (<em>CitySet</em>), comprising 20,589 RS images, 1.1 million SV images, and 0.8 million question–answer pairs. <em>CitySet</em> facilitates geospatial object reasoning, social object analysis, urban economic assessment, and sustainable development report generation. Besides, we develop <em>CityVLM</em> to integrate macro- and micro-level semantics using geospatial and temporal modeling, while its language modeling component generates detailed urban reports. We extensively benchmarked 10 advanced VLMs on our dataset, revealing that state-of-the-art models struggle with urban analysis tasks, primarily due to domain gaps and limited multi-view data alignment capabilities. By addressing these issues, <em>CityVLM</em> achieves superior performance consistently across all tasks and advances automated urban analysis through practical applications like heat island effect monitoring, offering valuable tools for city planners and policymakers in their sustainability efforts.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 62-74"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685023","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}

{"title":"AMS-Former: Adaptive multi-scale transformer for multi-modal image matching","authors":"Jiahao Rao ,&nbsp;Rui Liu ,&nbsp;Jianjun Guan ,&nbsp;Xin Tian","doi":"10.1016/j.isprsjprs.2026.01.021","DOIUrl":"10.1016/j.isprsjprs.2026.01.021","url":null,"abstract":"<div><div>Multi-modal image (MMI) matching plays a crucial role in the fusion of multi-source image information. However, due to the significant geometric and modality differences in MMI, existing methods often fail to achieve satisfactory matching performance. To address these challenges, we propose an end-to-end MMI matching approach, named adaptive multi-scale transformer (AMS-Former). First, AMS-Former constructs a multi-scale image matching framework that integrates contextual information across different scales, effectively identifying potential corresponding points and thereby improving matching accuracy. To handle the challenges caused by modality differences, we design a cross-modal feature extraction module with an adaptive modulation strategy. This module effectively couples features from different modalities, enhancing feature representation and improving model robustness under complex modality differences. To further enhance matching performance, we design a suitable loss function for the proposed AMS-Former to guide the optimization of network parameters. Finally, we use a cross-scale mutual supervision strategy to remove incorrect corresponding points and enhance the reliability of the matching results. Extensive experiments on five MMI datasets demonstrate that AMS-Former outperforms state-of-the-art methods, including RIFT, ASS, COFSM, POS-GIFT, Matchformer, SEMLA, TopicFM, and Lightglue. Our code is available at: <span><span>https://github.com/Henryrjh/AMS_Former</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 957-973"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001036","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}

{"title":"TFRSUB: A terrain-feature retention and spatial uniformity balancing method for simplifying LiDAR ground point clouds","authors":"Chuanfa Chen,&nbsp;Ziming Yang,&nbsp;Hongming Pan,&nbsp;Yanyan Li,&nbsp;Jinda Hao","doi":"10.1016/j.isprsjprs.2025.12.015","DOIUrl":"10.1016/j.isprsjprs.2025.12.015","url":null,"abstract":"<div><div>The processing of high-density LiDAR point clouds presents significant computational challenges for DEM generation due to data redundancy and topographic feature degradation during simplification. To overcome this problem, this study proposes a Terrain-Feature Retention and Spatial Uniformity Balancing (TFRSUB) method that integrates three key innovations: (i) a Distance-Geometric Synergy Index (DGSI) combining orthogonal deviation distance and point sampling interval to mitigate boundary contraction artifacts; (ii) a Composite Terrain Factor (CTF) synthesizing multiple terrain parameters to characterize diverse topographic features; and (iii) a cluster-driven Gaussian Process Regression (GPR) framework using CTF for iterative feature point selection, optimizing the trade-off between topographic fidelity and point distribution homogeneity. Evaluated on eight high-resolution LiDAR terrain point clouds across six retention ratios, TFRSUB demonstrates significant accuracy improvements over seven state-of-the-art methods, achieving reductions of 9.22%–64.70% in DEM root mean square error, 7.80%–61.34% in mean absolute error, 16.43%–76.88% in slope error, and 28.12%–81.35% in mean curvature error. These results establish TFRSUB as an alternative solution for LiDAR point cloud simplification that maintains topographic fidelity while addressing computational storage challenges.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 389-407"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884004","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}