ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Descriptor-based optical flow quality assessment and error model construction for visual localization","authors":"Jietao Lei ,&nbsp;Jingbin Liu ,&nbsp;Wei Zhang ,&nbsp;Mengxiang Li ,&nbsp;Juha Hyyppä","doi":"10.1016/j.isprsjprs.2025.01.019","DOIUrl":"10.1016/j.isprsjprs.2025.01.019","url":null,"abstract":"<div><div>Precise matching of visual features between frames is crucial for the robustness and accuracy of visual odometry and SLAM (Simultaneous Localization and Mapping) systems. However, factors such as complex illumination and texture variations may cause significant errors in feature correspondences that will degrade the accuracy of visual localization. In this paper, we utilize the feature descriptor to validate and assess the correspondence quality of the optical flow algorithm, and establish the information matrix of visual measurements, which is used for improving the accuracy of visual localization in the nonlinear optimization framework. This proposed approach of optical flow quality assessment leverages the complementary advantages of the optical flow algorithm and descriptor matching, and it is applicable to other visual odometry or SLAM systems that use the optical flow algorithm for feature correspondence. We first demonstrate through simulation experiments the statistical correlation between optical flow error and descriptor Hamming distance. Subsequently, based on the statistical correlation, the optical flow tracking error is quantitatively estimated using the descriptor Hamming distance. As a result, features with large tracking errors are rejected as outliers, and other features are remained with an adequate error model, i.e. information matrix in the nonlinear optimization, which corresponds with the visual tracking error. Furthermore, rather than direct tracking error between the initial observation frame and the current frame, we proposed the cumulative tracking error for successive frames (CTE-SF) to improve the efficiency of descriptor extraction in successive visual tracking, as it requires no the construction of multi-scale image pyramids. We evaluated the proposed solution using the open datasets and our developed in-house embedded positioning device. The results indicate that the proposed solution can improve the accuracy of visual odometry systems utilizing the optical flow algorithm for feature correspondence (e.g., VINS-Mono) by approximately 10%–50%, while requiring only an 11% increase in computational resource consumption. We have made our implementation open-source, available at: <span><span>https://github.com/Jett64/VINS-with-Error-Model</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 143-154"},"PeriodicalIF":10.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377412","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":"Image motion degradation compensation for high dynamic imaging of space-based vertical orbit scanning","authors":"Jiamin Du ,&nbsp;Xiubin Yang ,&nbsp;Zongqiang Fu ,&nbsp;Suining Gao ,&nbsp;Tianyu Zhang ,&nbsp;Jinyan Zou ,&nbsp;Xi He ,&nbsp;Shaoen Wang","doi":"10.1016/j.isprsjprs.2025.01.029","DOIUrl":"10.1016/j.isprsjprs.2025.01.029","url":null,"abstract":"<div><div>Rotating Payload Satellite (RPS) utilizes payload rotation to drive the optical axis for vertical orbit scanning, which enables high-resolution and wide-coverage imaging of ground curved targets. However, the presence of irregular image motion degradation (IMD) in the dynamic imaging drastically degrades the imaging quality. High stability and high precision IMD compensation have become key point for high-resolution imaging of RPS. In this paper, an IMD compensation model is proposed based on velocity vector prediction and multiple disturbance identification. Firstly, time-varying multi-dimensional velocity vectors are analyzed based on the object-to-image mapping relationship. This method is used to predict the rotation angle of the sensor, which can ensure the sensor’s exposure direction always follows the direction of image motion. Then, to enhance accuracy and stability of compensation, the actual angular velocity of sensor rotation is extracted from various disturbance sources through coordinate transformation and provided as feedback. The experiment indicates that the precision and stability of sensor rotation can reach 3.925 × 10^-3 and 8.574 × 10^-4 deg/s. The compensation error is smaller than the threshold of 1/3 pixel. The simulated images of RPS indicate that the deblurring and cumulative deformation correction effects are significant. The image quality is improved by 52.68 % after compensation. It demonstrates that our approach is highly effective and crucial for the practical application of RPS.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 124-142"},"PeriodicalIF":10.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372923","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":"Twin deformable point convolutions for airborne laser scanning point cloud classification","authors":"Yong-Qiang Mao ,&nbsp;Hanbo Bi ,&nbsp;Xuexue Li ,&nbsp;Kaiqiang Chen ,&nbsp;Zhirui Wang ,&nbsp;Xian Sun ,&nbsp;Kun Fu","doi":"10.1016/j.isprsjprs.2025.01.031","DOIUrl":"10.1016/j.isprsjprs.2025.01.031","url":null,"abstract":"<div><div>Thanks to the application of deep learning technology in point cloud processing of the remote sensing field, point cloud classification has become a research hotspot in recent years. Although existing solutions have made unprecedented progress, they ignore the inherent characteristics of point clouds in remote sensing fields that are strictly arranged according to latitude, longitude, and altitude, which brings great convenience to the segmentation of point clouds in remote sensing fields. To consider this property cleverly, we propose novel convolution operators, termed Twin Deformable point Convolutions (TDConvs), which aim to achieve adaptive feature learning by learning deformable sampling points in the latitude–longitude plane and altitude direction, respectively. First, to model the characteristics of the latitude–longitude plane, we propose a Cylinder-wise Deformable point Convolution (CyDConv) operator, which generates a two-dimensional cylinder map by constructing a cylinder-like grid in the latitude–longitude direction, and then performs adaptive feature sampling on the cylinder map by deformable offset learning. Furthermore, to better integrate the features of the latitude–longitude plane and the spatial geometric features, we perform a multi-scale fusion of the extracted latitude–longitude features and spatial geometric features, and realize it through the aggregation of adjacent point features of different scales. In addition, a Sphere-wise Deformable point Convolution (SpDConv) operator is introduced to adaptively offset the sampling points in three-dimensional space by constructing a sphere grid structure, aiming at modeling the characteristics in the altitude direction. Experiments on existing popular benchmarks conclude that our TDConvs achieve the best segmentation performance, surpassing existing advanced methods such as RFFS-Net and MCFN. Specifically, TDConvs achieves 73.4% mF1 on the ISPRS Vaihingen 3D dataset, which is 4.8% higher than the baseline. Details of the datasets used and the code is available on <span><span>https://github.com/WingkeungM/TDConvs</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 78-91"},"PeriodicalIF":10.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348228","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":"DALI-SLAM: Degeneracy-aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization","authors":"Weitong Wu ,&nbsp;Chi Chen ,&nbsp;Bisheng Yang ,&nbsp;Xianghong Zou ,&nbsp;Fuxun Liang ,&nbsp;Yuhang Xu ,&nbsp;Xiufeng He","doi":"10.1016/j.isprsjprs.2025.01.036","DOIUrl":"10.1016/j.isprsjprs.2025.01.036","url":null,"abstract":"<div><div>LiDAR-Inertial simultaneous localization and mapping (LI-SLAM) plays a crucial role in various applications such as robot localization and low-cost 3D mapping. However, factors including inaccurate motion distortion estimation and pose graph constraints, and frequent LiDAR feature degeneracy present significant challenges for existing LI-SLAM methods. To address these issues, we propose DALI-SLAM, an accurate and robust LI-SLAM that consists of degeneracy-aware LiDAR-inertial odometry (DA-LIO) with a dual spline-based motion distortion correction (DS-MDC) module, and multi-constraint pose graph optimization (MC-PGO). Considering the cumulative errors of micro-electromechanical systems (MEMS) inertial measurement unit (IMU) integration, two continuous-time trajectories in the sliding window are fitted to update the discrete IMU poses for accurate motion distortion correction. In the LiDAR-inertial fusion stage, LiDAR feature degeneracy is detected by analyzing the Jacobian matrix and a remapping strategy is introduced into the updating of error state Kalman Filter (ESKF) to mitigate the influence of degeneracy. Furthermore, in the back-end optimization stage, three types of submap constraints are accurately built with dedicated strategy through a robust variant of the iterative closest point (ICP) method. The proposed method is comprehensively validated using data collected from a helmet-based laser scanning system (HLS) in representative indoor and outdoor environments. Experiment results demonstrate that the proposed method outperforms the SOTA methods on the test data. Specifically, the proposed DS-MDC module reduces trajectory root mean square errors (RMSEs) by 7.9 %, 5.8 %, and 3.1 %, while the degeneracy-aware update strategy achieves additional reductions of 43.3 %, 17.7%, and 4.9 %, respectively, across three typical sequences compared to existing methods, thereby effectively improving trajectory accuracy. Furthermore, the results of DA-LIO demonstrate a maximum RMSE within 1 kilometer of approximately 1 meter in outdoor environments, achieving superior performance compared to the SOTA method FAST-LIO2. After performing MC-PGO, the RMSEs of the trajectories are reduced by 25.2 %, 9.2 %, and 52.4 %, respectively, across three typical sequences, demonstrating better performance compared to the SOTA method HBA. Code will be available at <span><span>https://github.com/DCSI2022/DALI_SLAM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 92-108"},"PeriodicalIF":10.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348229","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 novel framework for river organic carbon retrieval through satellite data and machine learning","authors":"Shang Tian ,&nbsp;Anmeng Sha ,&nbsp;Yingzhong Luo ,&nbsp;Yutian Ke ,&nbsp;Robert Spencer ,&nbsp;Xie Hu ,&nbsp;Munan Ning ,&nbsp;Yi Zhao ,&nbsp;Rui Deng ,&nbsp;Yang Gao ,&nbsp;Yong Liu ,&nbsp;Dongfeng Li","doi":"10.1016/j.isprsjprs.2025.01.028","DOIUrl":"10.1016/j.isprsjprs.2025.01.028","url":null,"abstract":"<div><div>Rivers transport large amounts of carbon, serving as a critical link between terrestrial, coastal, and atmospheric biogeochemical cycles. However, our observations and understanding of long-term river carbon dynamics in large-scale remain limited. Integrating machine learning with remote sensing offers an effective approach for quantifying organic carbon (OC) from space. Here, we develop the Aquatic-Organic Carbon (Aqua-OC), a dynamic machine learning retrieval framework designed to estimate reach-scale river OC using nearly half a century of analysis-ready Landsat archives. We first integrate a globally representative river OC dataset, comprising 299,330 measurements of dissolved organic carbon (DOC) and 101,878 measurements of particulate organic carbon (POC). This dataset is then used to evaluate the performance of four machine learning methods, i.e., random forest (RF), extreme gradient boosting (XGBoost), Support vector regression (SVR), and deep neural network (DNN), using an optical water type classification strategy. We further leverage multimodal input features to enhance the Aqua-OC framework and OC retrieval accuracy by considering various factors related to OC sources and environmental conditions. The results demonstrate that the Aqua-OC can effectively estimate DOC (R² = 0.68, RMSE = 2.88 mg/L, Bias = 2.63 %, Error = 12.52 %) and POC (R² = 0.76, RMSE = 1.76 mg/L, Bias = 6.31 %, Error = 21.36 %). Additionally, the Mississippi River Basin case study demonstrates Aqua-OC’s capability to map nearly four decades of reach-scale OC changes at a basin scale. This study provides a generalized method for satellite-based river OC retrieval at fine spatial and long-term temporal scales, thus offering an effective tool to quantify the rivers’ role in the global carbon cycle.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 109-123"},"PeriodicalIF":10.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348230","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":"SkyEyeGPT: Unifying remote sensing vision-language tasks via instruction tuning with large language model","authors":"Yang Zhan ,&nbsp;Zhitong Xiong ,&nbsp;Yuan Yuan","doi":"10.1016/j.isprsjprs.2025.01.020","DOIUrl":"10.1016/j.isprsjprs.2025.01.020","url":null,"abstract":"<div><div>Large language models (LLMs) have recently been extended to the vision-language realm, obtaining impressive general multi-modal capabilities. However, the exploration of multi-modal large language models (MLLMs) for remote sensing (RS) data is still in its infancy, lacking datasets and with unsatisfactory performance. In this work, we meticulously curate a large-scale RS multi-modal instruction tuning dataset, including single-task and multi-task conversation instructions. After manual verification, we obtain a high-quality RS instruction-following dataset with 968k samples, namely SkyEye-968k. To this end, we introduce SkyEyeGPT, a unified multi-modal large language model specifically designed for RS multi-granularity vision-language understanding. Our research demonstrates that with a simple yet effective design, SkyEyeGPT works surprisingly well on considerably different tasks without the need for extra encoding modules. Specifically, after projecting RS visual features to the language domain via an alignment layer, they are fed jointly with task-specific instructions into an LLM-based RS decoder to predict answers for RS open-ended tasks. In addition, we design a two-stage tuning method to enhance instruction-following and multi-turn dialogue ability at different granularities. Experiments on 8 datasets for RS vision-language tasks demonstrate SkyEyeGPT’s superiority in image-level and region-level tasks, such as captioning and visual grounding. In particular, SkyEyeGPT exhibits encouraging results compared to GPT-4V in some qualitative tests. The online demo, code, and dataset will be released.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 64-77"},"PeriodicalIF":10.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143197211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel scene coupling semantic mask network for remote sensing image segmentation","authors":"Xiaowen Ma ,&nbsp;Rongrong Lian ,&nbsp;Zhenkai Wu ,&nbsp;Renxiang Guan ,&nbsp;Tingfeng Hong ,&nbsp;Mengjiao Zhao ,&nbsp;Mengting Ma ,&nbsp;Jiangtao Nie ,&nbsp;Zhenhong Du ,&nbsp;Siyang Song ,&nbsp;Wei Zhang","doi":"10.1016/j.isprsjprs.2025.01.025","DOIUrl":"10.1016/j.isprsjprs.2025.01.025","url":null,"abstract":"<div><div>As a common method in the field of computer vision, spatial attention mechanism has been widely used in semantic segmentation of remote sensing images due to its outstanding long-range dependency modeling capability. However, remote sensing images are usually characterized by complex backgrounds and large intra-class variance that would degrade their analysis performance. While vanilla spatial attention mechanisms are based on dense affine operations, they tend to introduce a large amount of background contextual information and lack of consideration for intrinsic spatial correlation. To deal with such limitations, this paper proposes a novel scene-Coupling semantic mask network, which reconstructs the vanilla attention with scene coupling and local global semantic masks strategies. Specifically, <strong>scene coupling</strong> module decomposes scene information into global representations and object distributions, which are then embedded in the attention affinity processes. This Strategy effectively utilizes the intrinsic spatial correlation between features so that improve the process of attention modeling. Meanwhile, <strong>local global semantic masks</strong> module indirectly correlate pixels with the global semantic masks by using the local semantic mask as an intermediate sensory element, which reduces the background contextual interference and mitigates the effect of intra-class variance. By combining the above two strategies, we propose the model SCSM, which not only can efficiently segment various geospatial objects in complex scenarios, but also possesses inter-clean and elegant mathematical representations. Experimental results on four benchmark datasets demonstrate the effectiveness of the above two strategies for improving the attention modeling of remote sensing images. For example, compared to the recent advanced method LOGCAN++, the proposed SCSM has 1.2%, 0.8%, 0.2%, and 1.9% improvements on the LoveDA, Vaihingen, Potsdam, and iSAID datasets, respectively. The dataset and code are available at <span><span>https://github.com/xwmaxwma/rssegmentation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 44-63"},"PeriodicalIF":10.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143197213","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":"GEPT-Net: An efficient geometry enhanced point transformer for shield tunnel leakage segmentation","authors":"Jundi Jiang ,&nbsp;Yueqian Shen ,&nbsp;Jinhu Wang ,&nbsp;Jinguo Wang ,&nbsp;Chenyang Zhang ,&nbsp;Jingyi Wang ,&nbsp;Vagner Ferreira","doi":"10.1016/j.isprsjprs.2025.01.032","DOIUrl":"10.1016/j.isprsjprs.2025.01.032","url":null,"abstract":"<div><div>Subway shield tunnels have emerged as the preferred solution for urban transportation due to their convenience and safety. Constructed using prefabricated concrete segments, these tunnels exhibit structural stability. However, the segment joints and bolt holes are prone to groundwater infiltration under prolonged external stress, potentially compromising the lifespan of the shield tunnels. Consequently, effective detection methods are imperative to ensure the safe operation of these tunnels. Accurate data acquisition and precise extraction of leakage features are critical for detecting leakages in subway tunnels. This research introduces Efficient Geometry Enhanced Point Transformer Network (GEPT-Net), an innovative point cloud semantic segmentation network designed specifically for detecting tunnel leakage. GEPT-Net leverages the observation that leakages predominantly occur at segment joints and bolt holes, characterized by distinct geometric features and lower intensity. The network incorporates Fast Point Feature Histograms (FPFH) to effectively capture these geometric features from the input data. Additionally, we introduce a point cloud serialization technique utilizing space-filling curves, enabling the network to perceive a greater number of points within the same computational power, thereby balancing efficiency and accuracy. The Geometry Enhanced Channel Attention (GECA) Block is introduced to enhance the interaction between FPFH feature channels and intensity channels, enhancing the precise localization of leakage areas. Furthermore, the Lovasz Hinge Loss is employed to address the issue of extreme class imbalance. We constructed a tunnel leakage point cloud dataset, named S3DIS_leakage, comprising approximately 1,600 m between two stations, to train and evaluate the performance of our network. Experimental results demonstrate that GEPT-Net achieves superior performance in tunnel leakage semantic segmentation, attaining approximately 85 % mean Intersection over Union and 89 % accuracy for leakage classes, outperforming cutting-edge 2D and 3D networks by at least 12 %. Moreover, GEPT-Net maintains a remarkable balance between segmentation accuracy and computational efficiency, rendering it viable for practical engineering applications. This study not only establishes a robust approach for tunnel leakage detection but also paves the way for future research on the comprehensive segmentation of shield tunnel components. The proposed framework is available from the following github repository: <span><span>https://github.com/jdjiang312/GEPT-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 20-43"},"PeriodicalIF":10.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143197212","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 large-scale VHR parcel dataset and a novel hierarchical semantic boundary-guided network for agricultural parcel delineation","authors":"Hang Zhao ,&nbsp;Bingfang Wu ,&nbsp;Miao Zhang ,&nbsp;Jiang Long ,&nbsp;Fuyou Tian ,&nbsp;Yan Xie ,&nbsp;Hongwei Zeng ,&nbsp;Zhaoju Zheng ,&nbsp;Zonghan Ma ,&nbsp;Mingxing Wang ,&nbsp;Junbin Li","doi":"10.1016/j.isprsjprs.2025.01.034","DOIUrl":"10.1016/j.isprsjprs.2025.01.034","url":null,"abstract":"<div><div>Current agricultural parcels (AP) extraction faces two main limitations: (1) existing AP delineation methods fail to fully utilize low-level information (e.g., parcel boundary information), leading to unsatisfactory performance under certain circumstances; (2) the lack of large-scale, high-resolution AP benchmark datasets in China hinders comprehensive model evaluation and improvement. To address the first limitation, we develop a hierarchical semantic boundary-guided network (HBGNet) to fully leverage boundary semantics, thereby improving AP delineation. It integrates two branches, a core branch of AP feature extraction and an auxiliary branch related to boundary feature mining. Specifically, the boundary extract branch employes a module based on Laplace convolution operator to enhance the model’s awareness of parcel boundary. For AP feature extraction, a local–global context aggregation module is designed to enhance the semantic representation of AP, improving the adaptability across different AP scenarios. Meanwhile, a boundary-guided module is developed to enhance boundary details of high-level AP semantic information. Ultimately, a multi-grained feature fusion module is designed to enhance the capacity of HBGNet to extract APs with various sizes and shapes. Regarding the second limitation, we construct the first large-scale very high-resolution (VHR) agricultural parcel dataset (FHAPD) across seven different areas, covering more than 10,000 km², using data from GaoFen-1 (2-meter) and GaoFen-2 (1-meter). Detailed experiments are conducted on the FHAPD, a publicly European dataset (i.e., Al4boundaries), and medium-resolution Sentinel-2 images from the Netherlands and HBGNet is compared with other eight AP delineation methods. Results show that HBGNet outperforms the other eight methods in attribute and geometry accuracy. The Intersection over Union (IOU), F1-score of the boundary (F_bdy), and global total-classification (GTC) exceed other methods by 0.61 %-7.52 %, 0.8 %-36.3 %, and 1.7 %-31.8 %, respectively. It also effectively transfers to unseen regions. We conclude that the proposed HBGNet is an effective, advanced, and transferable method for diverse agricultural scenarios and remote sensing images.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"221 ","pages":"Pages 1-19"},"PeriodicalIF":10.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143259204","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":"Overcoming the uncertainty challenges in detecting building changes from remote sensing images","authors":"Jiepan Li ,&nbsp;Wei He ,&nbsp;Zhuohong Li ,&nbsp;Yujun Guo ,&nbsp;Hongyan Zhang","doi":"10.1016/j.isprsjprs.2024.11.017","DOIUrl":"10.1016/j.isprsjprs.2024.11.017","url":null,"abstract":"<div><div>Detecting building changes with multi-temporal remote sensing (RS) imagery at a very high resolution can help us understand urbanization and human activities, making informed decisions in urban planning, resource allocation, and infrastructure development. However, existing methods for building change detection (BCD) generally overlook critical uncertainty phenomena presented in RS imagery. Specifically, these uncertainties arise from two main sources: First, current building change detection datasets are designed primarily to detect changes in buildings, while changes in other land-cover classes are often classified as an unchanged background. Because of the manual labeling process, background elements that resemble buildings, such as roads and bridges, are at significant risk of being misclassified as building changes, introducing aleatoric uncertainty at the data level. Second, changes in parts of buildings that affect appearance, texture, or style without altering their semantic meaning, known as pseudo-changes, along with the imbalance between changed and unchanged samples, together lead to epistemic uncertainty at the model level. To address these challenges, we present an Uncertainty-Aware BCD (UA-BCD) framework. In detail, we employ a Siamese pyramid vision transformer to extract multi-level features from bi-temporal images, which are then decoded via a general decoder to obtain a coarse change map with inherent uncertainty. Subsequently, we introduce the aleatoric uncertainty estimation module to estimate the aleatoric uncertainty and embed it into the feature space. Then, a knowledge-guided feature enhancement module is developed to leverage the knowledge encoded in the coarse map to enhance the multi-level features and generate a refined change map. Finally, we propose an epistemic uncertainty estimator that takes the bi-temporal images and the refined change map as input and outputs an estimate of epistemic uncertainty. This estimation is supervised by the entropy calculated from the refined map, ensuring that the UA-BCD framework can produce a change map with lower epistemic uncertainty. To comprehensively validate the efficacy of the UA-BCD framework, we adopt a dual-perspective verification approach. Extensive experiments on five public building change datasets demonstrate the significant advantages of the proposed method over current state-of-the-art methods. Additionally, an application in Dongxihu District, Wuhan, China, confirms the outstanding performance of the proposed method in large-scale BCD. The source code of the project is available at <span><span>https://github.com/Henryjiepanli/UA-BCD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 1-17"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790040","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}