ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"RECREATE: Supervised contrastive learning and inpainting based hyperspectral image denoising","authors":"Aditya Dixit ,&nbsp;Anup Kumar Gupta ,&nbsp;Puneet Gupta ,&nbsp;Ankur Garg","doi":"10.1016/j.isprsjprs.2026.01.022","DOIUrl":"10.1016/j.isprsjprs.2026.01.022","url":null,"abstract":"<div><div>Hyperspectral image (HSI) contains information at various spectra, making it valuable in several real-world applications such as environmental monitoring, agriculture, and remote sensing. However, the acquisition process often introduces noise, necessitating effective HSI denoising methods to maintain its applicability. Deep Learning (DL) is considered as the de-facto for HSI denoising, but it requires a significant number of training samples to optimize network parameters for effective denoising outcomes. However, obtaining extensive datasets is challenging in HSI, leading to epistemic uncertainties and thereby deteriorating the denoising performance. This paper introduces a novel supervised contrastive learning (SCL) method, <em>RECREATE</em>, to enhance feature learning and mitigate the issue of epistemic uncertainty for HSI denoising. Furthermore, we introduce the exploration of image inpainting as an auxiliary task to enhance the HSI denoising performance. By adding HSI inpainting to CL, our method essentially enhances HSI denoising by increasing training datasets and enforcing improved feature learning. Experimental outcomes on various HSI datasets validate the efficacy of <em>RECREATE</em>, showcasing its potential for integration with existing HSI denoising techniques to enhance their performance, both qualitatively and quantitatively. This innovative method holds promise for addressing the limitations posed by limited training data and thereby advancing the field toward proposing better HSI denoising methods.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"233 ","pages":"Pages 14-24"},"PeriodicalIF":12.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982031","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":"Progressive uncertainty-guided network for binary segmentation in high-resolution remote sensing imagery","authors":"Jiepan Li ,&nbsp;Wei He ,&nbsp;Ting Hu ,&nbsp;Minghao Tang ,&nbsp;Liangpei Zhang","doi":"10.1016/j.isprsjprs.2026.01.010","DOIUrl":"10.1016/j.isprsjprs.2026.01.010","url":null,"abstract":"<div><div>Binary semantic segmentation in remote sensing (RS) imagery faces persistent challenges due to complex object appearances, ambiguous boundaries, and high similarity between foreground and background, all of which introduce significant uncertainty into the prediction process. Existing approaches often treat uncertainty as either a global attribute or a pixel-level estimate, overlooking the critical role of spatial and contextual interactions. To address these limitations, we propose the <strong>Progressive Uncertainty-Guided Segmentation Network (PUGNet)</strong>, a unified framework that explicitly models uncertainty in a context-aware manner. PUGNet decomposes uncertainty into three distinct components: <strong>foreground uncertainty</strong>, <strong>background uncertainty</strong>, and <strong>contextual uncertainty</strong>. This tripartite modeling enables more precise handling of local ambiguities and global inconsistencies. We adopt a coarse-to-fine decoding strategy that progressively refines features through two specialized modules. The <strong>Dynamic Uncertainty-Aware Module</strong> enhances regions of high foreground and background uncertainty using Gaussian-based modeling and contrastive learning. The <strong>Entropy-Driven Refinement Module</strong> quantifies contextual uncertainty via entropy and facilitates adaptive refinement through multi-scale context aggregation. Extensive experiments on ten public benchmark datasets, covering both single-temporal (<em>e.g.</em>, building and cropland extraction) and bi-temporal (<em>e.g.</em>, building change detection) binary segmentation tasks, demonstrate that PUGNet consistently achieves superior segmentation accuracy and uncertainty reduction, establishing a new state of the art in RS binary segmentation. The full implementation of the proposed framework and all experimental results can be accessed at <span><span>https://github.com/Henryjiepanli/PU_RS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 561-577"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925511","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":"Change tensor: Estimating complex topographic changes from point clouds using Riemann manifold surfaces","authors":"Shoujun Jia ,&nbsp;Lotte de Vugt ,&nbsp;Andreas Mayr ,&nbsp;Katharina Anders ,&nbsp;Chun Liu ,&nbsp;Martin Rutzinger","doi":"10.1016/j.isprsjprs.2026.01.009","DOIUrl":"10.1016/j.isprsjprs.2026.01.009","url":null,"abstract":"<div><div>Estimating complex 3D topographic surface changes including rigid spatial movement and non-rigid morphological deformation is an essential task to investigate Earth surface dynamics. However, for current 3D point comparison approaches, it is challenging to separate rigid and non-rigid topographic surface changes from multi-temporal 3D point clouds. Additionally, these methods are affected by challenges including topographic surface roughness and point cloud heterogeneities (i.e., discrete and irregular point distributions). To address these challenges, in this paper, we consider the dynamic evolution of topographic surfaces as the geometric changes of Riemann manifold surfaces. By building Euclidean (straight) and non-Euclidean (curved) coordinate systems on Riemann manifold surfaces that are represented from point clouds, the rigid transformation and non-rigid deformation of the Riemann manifold surfaces are solved to conceptualize rigid and non-rigid change tensors, respectively. On this basis, we design rigid (i.e., translation and rotation) and non-rigid (i.e., stretch and distortion) change features to describe various topographic surface changes and quantify the associated uncertainties to capture significant changes. The proposed method is tested on pairwise point clouds with simulated and real topographic surface changes in mountain regions. Simulation experiments demonstrate that the proposed method performed better than the baseline (i.e., M3C2) and state-of-the-art methods (i.e., LOG), with a higher translation accuracy (more than 50% improvement), a lower translation uncertainty (more than 61% reduction), and strong robustness to varying point densities. These results also show that the proposed method accurately quantifies three additional types of change features (i.e., the mean accuracies of rotation, stretch, and distortion are 1.5°, 0.5%, and 3.5°, respectively). Moreover, the real-scene experimental results demonstrate the effectiveness and superiority of the proposed method in estimating various topographic changes in real environments, the applicability in analyzing geomorphological processes, and the potential contribution for understanding spatiotemporal patterns of Earth surface dynamics.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 766-786"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957294","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":"MARSNet: A Mamba-driven adaptive framework for robust multisource remote sensing image matching in noisy environments","authors":"Weipeng Jing ,&nbsp;Peilun Kang ,&nbsp;Donglin Di ,&nbsp;Jian Wang ,&nbsp;Yang Song ,&nbsp;Chao Li ,&nbsp;Lei Fan","doi":"10.1016/j.isprsjprs.2025.12.021","DOIUrl":"10.1016/j.isprsjprs.2025.12.021","url":null,"abstract":"<div><div>Semi-dense matching of multi-source remote sensing images under noise interference remains a challenging task. Existing detector-free methods often exhibit low efficiency and reduced performance when faced with large viewpoint variations and significant noise disturbances. Due to the inherent noise and modality differences in multi-source remote sensing images, the accuracy and robustness of feature matching are substantially compromised. To address this issue, we propose a hybrid network for multi-source remote sensing image matching based on an efficient and robust Mamba framework, named MARSNet. The network achieves efficient and robust matching through the following innovative designs: First, it leverages the efficient Mamba network to capture long-range dependencies within image sequences, enhancing the modeling capability for complex scenes. Second, a frozen pre-trained DINOv2 foundation model is introduced as a robust feature extractor, effectively improving the model’s noise resistance. Finally, an adaptive fusion strategy is employed to integrate features, and the Mamba-like linear attention mechanism is adopted to refine the Transformer-based linear attention, further enhancing the efficiency and expressive power for long-sequence processing. To validate the effectiveness of the proposed method, extensive experiments were conducted on multi-source remote sensing image datasets, covering various scenarios such as noise-free, additive random noise, and periodic stripe noise. The experimental results demonstrate that the proposed method achieves significant improvements in matching accuracy and robustness compared to state-of-the-art methods. Additionally, by performing pose error evaluation on a large-scale general dataset, the superior performance of the proposed method in 3D reconstruction is validated, complementing the test results from the multi-source remote sensing dataset, thereby providing a more comprehensive assessment of the method’s generalization ability and robustness.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 457-475"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903503","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":"DVGBench: Implicit-to-explicit visual grounding benchmark in UAV imagery with large vision–language models","authors":"Yue Zhou ,&nbsp;Jue Chen ,&nbsp;Zilun Zhang ,&nbsp;Penghui Huang ,&nbsp;Ran Ding ,&nbsp;Zhentao Zou ,&nbsp;PengFei Gao ,&nbsp;Yuchen Wei ,&nbsp;Ke Li ,&nbsp;Xue Yang ,&nbsp;Xue Jiang ,&nbsp;Hongxin Yang ,&nbsp;Jonathan Li","doi":"10.1016/j.isprsjprs.2026.01.005","DOIUrl":"10.1016/j.isprsjprs.2026.01.005","url":null,"abstract":"<div><div>Remote sensing (RS) large vision–language models (LVLMs) have shown strong promise across visual grounding (VG) tasks. However, existing RS VG datasets predominantly rely on explicit referring expressions – such as relative position, relative size, and color cues – thereby constraining performance on implicit VG tasks that require scenario-specific domain knowledge. This article introduces DVGBench, a high-quality implicit VG benchmark for drones, covering six major application scenarios: traffic, disaster, security, sport, social activity, and productive activity. Each object provides both explicit and implicit queries. Based on the dataset, we design DroneVG-R1, an LVLM that integrates the novel Implicit-to-Explicit Chain-of-Thought (I2E-CoT) within a reinforcement learning paradigm. This enables the model to take advantage of scene-specific expertise, converting implicit references into explicit ones and thus reducing grounding difficulty. Finally, an evaluation of mainstream models on both explicit and implicit VG tasks reveals substantial limitations in their reasoning capabilities. These findings provide actionable insights for advancing the reasoning capacity of LVLMs for drone-based agents. The code and datasets will be released at <span><span>https://github.com/zytx121/DVGBench</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 831-847"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977407","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":"Attributing GHG emissions to individual facilities using multi-temporal hyperspectral images: Methodology and applications","authors":"Yichi Zhang ,&nbsp;Ge Han ,&nbsp;Yiyang Huang ,&nbsp;Huayi Wang ,&nbsp;Hongyuan Zhang ,&nbsp;Zhipeng Pei ,&nbsp;Yuanxue Pu ,&nbsp;Haotian Luo ,&nbsp;Jinchun Yi ,&nbsp;Tianqi Shi ,&nbsp;Siwei Li ,&nbsp;Wei Gong","doi":"10.1016/j.isprsjprs.2026.01.014","DOIUrl":"10.1016/j.isprsjprs.2026.01.014","url":null,"abstract":"<div><div>Industrial parks are major sources of greenhouse gas (GHG) emissions and the ultimate entities responsible for implementing mitigation policies. Current satellite remote sensing technologies perform well in reporting localized strong point-source emissions, but face significant challenges in monitoring emissions from multiple densely clustered sources. To address the limitation, we propose an emission allocation framework, EA-MILES, which integrates multi-source hyperspectral data with plume modeling to quantify process-level emissions. Simulation experiments show that with existing hyperspectral satellites, EA-MILES can estimate emissions for sources with intensities above 80 t CO₂/h and 100 kg CH₄/h with bias not exceed 13.60 % and 17.08 %. A steel and power production park is selected as a case study, where EA-MILES estimates process-level emissions with uncertainties ranging from 26.33 % to 37.78 %. Estimation results are consistent with inventory values derived from emission factor methods. Top-down Integrated Mass Enhancement method is utilized to compare with EA-MILES results, the estimation bias did not exceed 16.84 %. According to the <em>Climate TRACE</em>, about 32 % of CO₂ and 44 % of CH₄ point-sources worldwide fall within EA-MILES detection coverage, accounting for over 80 % and 55 % of anthropogenic CO₂ and CH₄ emissions. Therefore, this study provides a novel satellite-based approach for reporting facility-scale GHG emissions in industrial parks, offering transparent and accurate monitoring data to support the mitigation and energy transition decision-making.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 937-956"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976628","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":"SAR-NanoShipNet: A scale-adaptive network for robust small ship detection in SAR imagery","authors":"Yuhao Zhang ,&nbsp;Jieru Chi ,&nbsp;Guowei Yang ,&nbsp;Chenglizhao Chen ,&nbsp;Teng Yu","doi":"10.1016/j.isprsjprs.2025.12.006","DOIUrl":"10.1016/j.isprsjprs.2025.12.006","url":null,"abstract":"<div><div>Currently, notable progress has been attained in small ship target detection for synthetic aperture radar (SAR) imagery, with such advancements being driven by three key methodological innovations within the deep learning framework: self-supervision combined with knowledge distillation, rotated bounding box detection, and multi-scale feature fusion. However, it still faces challenges such as high speckle noise in SAR images, difficulty in extracting small target features, geometric distortion of ship shapes and heading dependence. Therefore, this article proposes a new SAR-NanoShipNet model. To enhance the targeting of ship objects, the proposed method employs a specialized convolution (DABConv) that exhibits greater suitability for ship targets, replacing the conventional standard convolution. As opposed to traditional approaches for SAR target detection, which typically lack the capability to adaptively capture the irregular boundaries and low-contrast features of small ship targets in SAR images, this method pioneers the adaptive capture of these features through deformable convolutions and boundary attention mechanisms, leading to enhanced target localization accuracy. In addition, we introduce the VerticalCompSPPF module (VC-SPPF), which incorporates longitudinal multi-scale convolution alongside a channel attention mechanism. Finally, the design of D-CLEM is linked with DABConv to enhance directional feature extraction while also fusing, improving the accuracy of small object detection. We have validated the superiority of our method on five datasets, particularly for high precision detection of small targets (AP<span><math><msub><mrow></mrow><mrow><mi>s</mi></mrow></msub></math></span> <span><math><mi>↑</mi></math></span>2.66%). Our code can be found at <span><span>https://github.com/Z-Yuhao/1.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 262-279"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785304","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":"Improvement of the consistency among long-term global land surface phenology products derived from AVHRR, MODIS, and VIIRS observations","authors":"Yongchang Ye ,&nbsp;Xiaoyang Zhang ,&nbsp;Yu Shen ,&nbsp;Khuong H. Tran ,&nbsp;Shuai Gao ,&nbsp;Yuxia Liu ,&nbsp;Shuai An","doi":"10.1016/j.isprsjprs.2025.12.010","DOIUrl":"10.1016/j.isprsjprs.2025.12.010","url":null,"abstract":"<div><div>Land surface phenology (LSP) has been widely derived from observations of different satellite sensors, including the Advanced Very High-Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). However, the consistency of long-term LSP products is a major concern because the time series data quality may vary greatly, particularly due to temporal gaps caused by cloud contamination, instrumental degradations (e.g., orbital drift), and other factors. Therefore, this study investigated the reconstruction of the high-quality time series of vegetation indices using a Spatiotemporal Shape-Matching Model (SSMM) and the reduction of the temporal gap impacts on LSP detections globally at the climate modeling grid (0.05°). Specifically, we first generated the climatology of a 3-day two-band Enhanced Vegetation Index (EVI2) using the MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset from 2003 to 2022. The temporal climatology EVI2 was used in the SSMM algorithm to fuse the 3-day time series of EVI2 data derived separately from five different surface reflectance products: AVHRR reflectance data (1981–2019), MODIS standard surface reflectance (SSR) and NBAR data (2000–2023), and VIIRS SSR and NBAR data (2012–2023). These five sets of EVI2 time series were further applied to detect LSP metrics. The result indicates that the coefficient of determination (R²) increased by up to 0.2 among the fused EVI2 time series from AVHRR, MODIS SSR, VIIRS SSR, MODIS NBAR, and VIIRS NBAR compared to that among the raw EVI2 time series. Although the AVHRR EVI2 dataset was more consistent with MODIS SSR or VIIRS SSR observations than with MODIS NBAR or VIIRS NBAR datasets, the highest R² was found between MODIS and VIIRS NBAR EVI2, especially between their fused EVI2 time series. Consequently, the mean absolute difference (MAD) of LSP metrics was reduced by one to three days in comparing fused EVI2 with raw EVI2 time series between two different sensors. Overall, the highest LSP consistency was found between fused MODIS NBAR and fused VIIRS NBAR, which was followed by LSP detections between raw MODIS NBAR and raw VIIRS NBAR, fused MODIS SSR and fused AVHRR, raw MODIS SSR and raw AVHRR, fused MODIS NBAR and fused AVHRR, and raw MODIS NBAR and raw AVHRR. The result suggests that long-term LSP products from 1980 forward should be generated using the fused EVI2 time series from AVHRR, MODIS SSR, and VIIRS SSR, while the product from 2000 forward should be produced using the fused time series from MODIS NBAR and VIIRS NBAR observations.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 369-388"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845309","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":"City-Facade: A city-level large-scale point cloud building facade dataset for semantic & instance segmentation","authors":"Yiping Chen ,&nbsp;Jonathan Li ,&nbsp;Ting Han ,&nbsp;Huifang Feng ,&nbsp;Jun Chen ,&nbsp;Cheng Wang","doi":"10.1016/j.isprsjprs.2026.01.003","DOIUrl":"10.1016/j.isprsjprs.2026.01.003","url":null,"abstract":"<div><div>With the growing demand for high-quality 3D urban scene understanding in applications such as building information modeling (BIM) and digital twins, large-scale and well-annotated 3D datasets have become essential for advancing scientific research and algorithm development. However, existing building facade datasets are predominantly image-based, suffering from drawbacks such as a lack of spatial information and sensitivity to lighting and weather conditions. Moreover, publicly available large-scale labeled datasets of building point clouds still remain scarce and have a relatively small coverage area. To this end, we introduce a <strong>city-level</strong> building facade point cloud dataset named <strong>City-Facade</strong> for semantic-level and instance-level segmentation. Firstly, the paper conducts a comprehensive review and analysis of existing urban &amp; building point cloud datasets and point cloud segmentation algorithms. Secondly, we present a large-scale building facade dataset with approximately <strong>200 millions of labeled 3D point clouds (over 60 km roads)</strong> belonging to urban scenarios, realized to facilitate the development and evaluation of <strong>semantic and instance level algorithms</strong> in the urban understanding. Finally, baseline experiments for semantic and instance segmentation are conducted to encourage further research. The proposed dataset is accessible at <span><span>https://github.com/gorgeouseping/City-Facade</span><svg><path></path></svg></span>, comprising the dataset and segmentation baselines for better comparison and presentation of strengths and weaknesses of different methods. Additionally, the data will undergo continuous improvement and updates based on feedback from the community.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 675-688"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925542","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":"An enhanced spatiotemporal prediction method on landslide displacement with LDP-ConvFormer and MT-InSAR observations","authors":"Jianao Cai ,&nbsp;Dongping Ming ,&nbsp;Feng Liu ,&nbsp;Wenyi Zhao ,&nbsp;Mingzhi Zhang ,&nbsp;Xiao Ling ,&nbsp;Mengyuan Zhu ,&nbsp;Lu Xu ,&nbsp;Tingting Lu ,&nbsp;Ningjie Liu ,&nbsp;Yanfei Wei ,&nbsp;Ming Huang","doi":"10.1016/j.isprsjprs.2025.12.027","DOIUrl":"10.1016/j.isprsjprs.2025.12.027","url":null,"abstract":"<div><div>Landslide Displacement Prediction (LDP) implementation for Landslide Early Warning Systems (LEWS) using the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique poses significant challenges in the Three Gorges Reservoir Area (TGRA). On the one hand, the limited revisit frequency of satellites fails to satisfy the high-frequency monitoring requirements of LEWS. On the other hand, traditional LDP methods concentrate on single-point modeling. It neglects the spatial correlation between displacement points and the landslide surface. To enhance the low-frequency MT-InSAR observations, this paper proposes a new hybrid algorithm that integrates the Kalman Filter (KF) and LDP-ConvFormer to achieve enhanced spatiotemporal LDP. First, multi-orbit MT-InSAR measurements are transformed to downslope displacement. Subsequently, the multi-orbit downslope displacements are integrated by KF to generate time series data with enhanced temporal resolution (5/7-day intervals). The KF estimations indicate that the integrated higher-resolution time series achieves high accuracy, with an RMSE of 0.431 cm and an R² of 0.974 compared to GNSS. Finally, to overcome the limitation of single-point modeling, a novel LDP-ConvFormer is constructed for enhanced spatiotemporal LDP. The Spatiotemporal Displacement Prediction Transformer (STDP-Former) employs Local Spatial Multi-Head Self-Attention (LSMHSA) and Temporal Multi-Head Self-Attention (TMHSA) to capture the displacement dependencies between different spatial locations at the same time steps and temporal relationships across different time steps, respectively. Additionally, the spatiotemporal feature map is decomposed into trend and periodic components, which are modeled separately and then summed for final predictions. Experimental results demonstrate that the constructed model can accurately establish the nonlinear relationship between the landslide displacement and its triggering factors. The LDP-ConvFormer outperforms benchmark methods, achieving RMSE: 46.29 mm, MAE: 26.7 mm, SSIM: 0.8187, PSNR: 35.62, R²: 0.9574, and EVar: 0.9603. Moreover, LDP-ConvFormer shows notable superiority in LDP over medium to long periods (60-90d) in the TGRA. The enhanced spatiotemporal LDP method provides extremely valuable reference for LEWS of translational landslides in the TGRA.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"232 ","pages":"Pages 594-612"},"PeriodicalIF":12.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925543","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}