ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point clouds","authors":"Zhaiyu Chen ,&nbsp;Yilei Shi ,&nbsp;Liangliang Nan ,&nbsp;Zhitong Xiong ,&nbsp;Xiao Xiang Zhu","doi":"10.1016/j.isprsjprs.2024.09.031","DOIUrl":"10.1016/j.isprsjprs.2024.09.031","url":null,"abstract":"<div><div>We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposition via graph node classification, achieving a watertight and compact reconstruction. To effectively represent arbitrary-shaped polyhedra in the neural network, we propose a skeleton-based sampling strategy to generate polyhedron-wise queries. These queries are then incorporated with inter-polyhedron adjacency to enhance the classification. PolyGNN is end-to-end optimizable and is designed to accommodate variable-size input points, polyhedra, and queries with an index-driven batching technique. To address the abstraction gap between existing city-building models and the underlying instances, and provide a fair evaluation of the proposed method, we develop our method on a large-scale synthetic dataset with well-defined ground truths of polyhedral labels. We further conduct a transferability analysis across cities and on real-world point clouds. Both qualitative and quantitative results demonstrate the effectiveness of our method, particularly its efficiency for large-scale reconstructions. The source code and data are available at <span><span>https://github.com/chenzhaiyu/polygnn</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 693-706"},"PeriodicalIF":10.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426646","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":"Clustering, triangulation, and evaluation of 3D lines in multiple images","authors":"Dong Wei ,&nbsp;Haoyu Guo ,&nbsp;Yi Wan ,&nbsp;Yongjun Zhang ,&nbsp;Chang Li ,&nbsp;Guangshuai Wang","doi":"10.1016/j.isprsjprs.2024.10.001","DOIUrl":"10.1016/j.isprsjprs.2024.10.001","url":null,"abstract":"<div><div>Three-dimensional (3D) lines require further enhancement in both clustering and triangulation. Line clustering assigns multiple image lines to a single 3D line to eliminate redundant 3D lines. Currently, it depends on the fixed and empirical parameter. However, a loose parameter could lead to over-clustering, while a strict one may cause redundant 3D lines. Due to the absence of the ground truth, the assessment of line clustering remains unexplored. Additionally, 3D line triangulation, which determines the 3D line segment in object space, is prone to failure due to its sensitivity to positional and camera errors.</div><div>This paper aims to improve the clustering and triangulation of 3D lines and to offer a reliable evaluation method. (1) To achieve accurate clustering, we introduce a probability model, which uses the prior error of the structure from the motion, to determine adaptive thresholds; thus controlling false clustering caused by the fixed hyperparameter. (2) For robust triangulation, we employ a universal framework that refines the 3D line with various forms of geometric consistency. (3) For a reliable evaluation, we investigate consistent patterns in urban environments to evaluate the clustering and triangulation, eliminating the need to manually draw the ground truth.</div><div>To evaluate our method, we utilized datasets of Internet image, totaling over ten thousand images, alongside aerial images with dimensions exceeding ten thousand pixels. We compared our approach to state-of-the-art methods, including Line3D++, Limap, and ELSR. In these datasets, our method demonstrated improvements in clustering and triangulation accuracy by at least 20% and 3%, respectively. Additionally, our method ranked second in execution speed, surpassed only by ELSR, the current fastest algorithm. The C++ source code for the proposed algorithm, along with the dataset used in this paper, is available at <span><span>https://github.com/weidong-whu/3DLineResconstruction</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 678-692"},"PeriodicalIF":10.6,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426645","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":"HyperDehazing: A hyperspectral image dehazing benchmark dataset and a deep learning model for haze removal","authors":"Hang Fu ,&nbsp;Ziyan Ling ,&nbsp;Genyun Sun ,&nbsp;Jinchang Ren ,&nbsp;Aizhu Zhang ,&nbsp;Li Zhang ,&nbsp;Xiuping Jia","doi":"10.1016/j.isprsjprs.2024.09.034","DOIUrl":"10.1016/j.isprsjprs.2024.09.034","url":null,"abstract":"<div><div>Haze contamination severely degrades the quality and accuracy of optical remote sensing (RS) images, including hyperspectral images (HSIs). Currently, there are no paired benchmark datasets containing hazy and haze-free scenes in HSI dehazing, and few studies have analyzed the distributional properties of haze in the spatial and spectral domains. In this paper, we developed a new hazy synthesis strategy and constructed the first hyperspectral dehazing benchmark dataset (HyperDehazing), which contains 2000 pairs synthetic HSIs covering 100 scenes and another 70 real hazy HSIs. By analyzing the distribution characteristics of haze, we further proposed a deep learning model called HyperDehazeNet for haze removal from HSIs. Haze-insensitive longwave information injection, novel attention mechanisms, spectral loss function, and residual learning are used to improve dehazing and scene reconstruction capability. Comprehensive experimental results demonstrate that the HyperDehazing dataset effectively represents complex haze in real scenes with synthetic authenticity and scene diversity, establishing itself as a new benchmark for training and assessment of HSI dehazing methods. Experimental results on the HyperDehazing dataset demonstrate that our proposed HyperDehazeNet effectively removes complex haze from HSIs, with outstanding spectral reconstruction and feature differentiation capabilities. Furthermore, additional experiments conducted on real HSIs as well as the widely used Landsat-8 and Sentinel-2 datasets showcase the exceptional dehazing performance and robust generalization capabilities of HyperDehazeNet. Our method surpasses other state-of-the-art methods with high computational efficiency and a low number of parameters.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 663-677"},"PeriodicalIF":10.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426644","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":"Vision-guided robot calibration using photogrammetric methods","authors":"Markus Ulrich ,&nbsp;Carsten Steger ,&nbsp;Florian Butsch ,&nbsp;Maurice Liebe","doi":"10.1016/j.isprsjprs.2024.09.037","DOIUrl":"10.1016/j.isprsjprs.2024.09.037","url":null,"abstract":"<div><div>We propose novel photogrammetry-based robot calibration methods for industrial robots that are guided by cameras or 3D sensors. Compared to state-of-the-art methods, our methods are capable of calibrating the robot kinematics, the hand–eye transformations, and, for camera-guided robots, the interior orientation of the camera simultaneously. Our approach uses a minimal parameterization of the robot kinematics and hand–eye transformations. Furthermore, it uses a camera model that is capable of handling a large range of complex lens distortions that can occur in cameras that are typically used in machine vision applications. To determine the model parameters, geometrically meaningful photogrammetric error measures are used. They are independent of the parameterization of the model and typically result in a higher accuracy. We apply a stochastic model for all parameters (observations and unknowns), which allows us to assess the precision and significance of the calibrated model parameters. To evaluate our methods, we propose novel procedures that are relevant in real-world applications and do not require ground truth values. Experiments on synthetic and real data show that our approach improves the absolute positioning accuracy of industrial robots significantly. By applying our approach to two different uncalibrated UR3e robots, one guided by a camera and one by a 3D sensor, we were able to reduce the RMS evaluation error by approximately 85% for each robot.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 645-662"},"PeriodicalIF":10.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426560","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 phenological-knowledge-independent method for automatic paddy rice mapping with time series of polarimetric SAR images","authors":"Suya Lin ,&nbsp;Zhixin Qi ,&nbsp;Xia Li ,&nbsp;Hui Zhang ,&nbsp;Qianwen Lv ,&nbsp;Di Huang","doi":"10.1016/j.isprsjprs.2024.09.035","DOIUrl":"10.1016/j.isprsjprs.2024.09.035","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Paddy rice, which sustains more than half of the global population, requires accurate and efficient mapping to ensure food security. Synthetic aperture radar (SAR) has become indispensable in this process due to its remarkable ability to operate effectively in adverse weather conditions and its sensitivity to paddy rice growth. Phenological-knowledge-based (PKB) methods have been commonly employed in conjunction with time series of SAR images for paddy rice mapping, primarily because they eliminate the need for training datasets. However, PKB methods possess inherent limitations, primarily stemming from their reliance on precise phenological information regarding paddy rice growth. This information varies across regions and paddy rice varieties, making it challenging to use PKB methods effectively on a large spatial scale, such as the national or global scale, where collecting comprehensive phenological data becomes impractical. Moreover, variations in farming practices and field conditions can lead to differences in paddy rice growth stages even within the same region. Using a generalized set of phenological knowledge in PKB methods may not be suitable for all paddy fields, potentially resulting in errors in paddy rice extraction. To address the challenges posed by PKB methods, this study proposed an innovative approach known as the phenological-knowledge-independent (PKI) method for mapping paddy rice using time series of Sentinel-1 SAR images. The central innovation of the PKI method lies in its capability to map paddy rice without relying on specific knowledge of paddy rice phenology or the need for a training dataset. This was made possible by the incorporation of three novel metrics: VH and VV normalized maximum temporal changes (NMTC) and VH temporal mean, derived from the distinctions between paddy rice and other land cover types in time series of SAR images. The PKI method was rigorously evaluated across three regions in China, each featuring different paddy rice varieties. Additionally, the PKI method was compared with two prevalent phenological-knowledge-based techniques: the automated paddy rice mapping method using SAR flooding signals (ARM-SARFS) and the manual interpretation of unsupervised clustering results (MI-UCR). The PKI method achieved an average overall accuracy of 97.99%, surpassing the ARM-SARFS, which recorded an accuracy of 89.65% due to errors stemming from phenological disparities among different paddy fields. Furthermore, the PKI method delivered results on par with the MI-UCR, which relied on the fusion of SAR and optical image time series, achieving an accuracy of 97.71%. As demonstrated by these findings, the PKI method proves highly effective in mapping paddy rice across diverse regions, all without the need for phenological knowledge or a training dataset. Consequently, it holds substantial promise for efficiently mapping paddy rice on a large spatial scale. The source code used in this study is availa","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 628-644"},"PeriodicalIF":10.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426561","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":"Variational Autoencoder with Gaussian Random Field prior: Application to unsupervised animal detection in aerial images","authors":"Hugo Gangloff ,&nbsp;Minh-Tan Pham ,&nbsp;Luc Courtrai ,&nbsp;Sébastien Lefèvre","doi":"10.1016/j.isprsjprs.2024.09.028","DOIUrl":"10.1016/j.isprsjprs.2024.09.028","url":null,"abstract":"<div><div>In real world datasets of aerial images, the objects of interest are often missing, hard to annotate and of varying aspects. The framework of unsupervised Anomaly Detection (AD) is highly relevant in this context, and Variational Autoencoders (VAEs), a family of popular probabilistic models, are often used. We develop on the literature of VAEs for AD in order to take advantage of the particular textures that appear in natural aerial images. More precisely we propose a new VAE model with a Gaussian Random Field (GRF) prior (VAE-GRF), which generalizes the classical VAE model, and we provide the necessary procedures and hypotheses required for the model to be tractable. We show that, under some assumptions, the VAE-GRF largely outperforms the traditional VAE and some other probabilistic models developed for AD. Our results suggest that the VAE-GRF could be used as a relevant VAE baseline in place of the traditional VAE with very limited additional computational cost. We provide competitive results on the MVTec reference dataset for visual inspection, and two other datasets dedicated to the task of unsupervised animal detection in aerial images.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 600-609"},"PeriodicalIF":10.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426557","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":"OR-LIM: Observability-aware robust LiDAR-inertial-mapping under high dynamic sensor motion","authors":"Yangzi Cong ,&nbsp;Chi Chen ,&nbsp;Bisheng Yang ,&nbsp;Ruofei Zhong ,&nbsp;Shangzhe Sun ,&nbsp;Yuhang Xu ,&nbsp;Zhengfei Yan ,&nbsp;Xianghong Zou ,&nbsp;Zhigang Tu","doi":"10.1016/j.isprsjprs.2024.09.036","DOIUrl":"10.1016/j.isprsjprs.2024.09.036","url":null,"abstract":"<div><div>Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D data. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. In this paper, we present OR-LIM, a novel observability-aware LiDAR-inertial-mapping system. Essentially, it combines a robust real-time LiDAR-inertial-odometry (LIO) module with an efficient surfel-map-smoothing (SMS) module that seamlessly optimizes the sensor poses and scene geometry at the same time. To improve robustness, the planar surfels are hierarchically generated and grown from point cloud maps to provide reliable correspondences for fixed-lag optimization. Moreover, the normals of surfels are analyzed for the observability evaluation of each frame. To maintain global consistency, a factor graph is utilized integrating the information from IMU propagation, LIO as well as the SMS. The system is extensively tested on the datasets collected by a low-cost multi-beam LiDAR (MBL) mounted on a rotating platform. The experiments with various settings of sensor motion, conducted on complex multi-story buildings and large-scale outdoor scenes, demonstrate the superior performance of our system over multiple state-of-the-art methods. The improvement of point accuracy reaches 3.39–13.6 % with an average 8.71 % outdoor and correspondingly 1.89–15.88 % with 9.09 % indoor, with reference to the collected Terrestrial Laser Scanning (TLS) map.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 610-627"},"PeriodicalIF":10.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426559","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":"Re-evaluating winter carbon sink in Southern Ocean by recovering MODIS-Aqua chlorophyll-a product at high solar zenith angles","authors":"Ke Zhang ,&nbsp;Zhaoru Zhang ,&nbsp;Jianfeng He ,&nbsp;Walker O. Smith ,&nbsp;Na Liu ,&nbsp;Chengfeng Le","doi":"10.1016/j.isprsjprs.2024.09.033","DOIUrl":"10.1016/j.isprsjprs.2024.09.033","url":null,"abstract":"<div><div>Satellite ocean color observations are extensively utilized in global carbon sink evaluation. However, the valid coverage of chlorophyll-a concentration (Chla, mg m⁻³) measurements from these observations is severely limited during autumn and winter in high latitude oceans. The high solar zenith angle (SZA) stands as one of the primary contributors to the reduced quality of Chla products in the high-latitude Southern Ocean during these seasons. This study addresses this challenge by employing a random forest-based regression ensemble (RFRE) method to enhance the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Chla products affected by high SZA conditions. The RFRE model incorporates the color index (CI), band-ratio index (R), SZA, sensor zenith angle (senz), and Rayleigh-corrected reflectance at 869 nm (Rrc(869)) as predictors. The results indicate that the RFRE model significantly increased the MODIS observed Chla coverage (1.03 to 3.24 times) in high-latitude Southern Ocean regions to the quality of standard Chla products. By applying the recovered Chla to re-evaluate the carbon sink in South Ocean, results showed that the Southern Ocean’s ability to absorb carbon dioxide (CO₂) in winter has been underestimated (5.9–18.6 Tg C year⁻¹) in previous assessments. This study underscores the significance of improving the Chla products for a more accurate estimation of winter carbon sink in the Southern Ocean.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 588-599"},"PeriodicalIF":10.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426556","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 new methodology for establishing an SOC content prediction model that is spatiotemporally transferable at multidecadal and intercontinental scales","authors":"Xiangtian Meng ,&nbsp;Yilin Bao ,&nbsp;Chong Luo ,&nbsp;Xinle Zhang ,&nbsp;Huanjun Liu","doi":"10.1016/j.isprsjprs.2024.09.038","DOIUrl":"10.1016/j.isprsjprs.2024.09.038","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Quantifying and tracking the soil organic carbon (SOC) content is a key step toward long-term terrestrial ecosystem monitoring. Over the past decade, numerous models have been proposed and have achieved promising results for predicting SOC content. However, many of these studies are confined to specific temporal or spatial contexts, neglecting model transferability. Temporal transferability refers to a model’s ability to be applied across different periods, while spatial transferability relates to its applicability across diverse geographic locations for prediction. Therefore, developing a new methodology to establish a prediction model with high spatiotemporal transferability for SOC content is critically important. In this study, two large intercontinental study areas were selected, and measured topsoil (0–20 cm) sample data, 27,059 cloudless Landsat 5/8 images, digital elevation models, and climate data were acquired for 3 periods. Based on these data, monthly average climate data, monthly average data reflecting soil properties, and topography data were calculated as original input (OI) variables. We established an innovative multivariate deep learning model with high spatiotemporal transferability, combining the advantages of attention mechanism, graph neural network, and long short-term memory network model (A-GNN-LSTM). Additionally, the spatiotemporal transferability of A-GNN-LSTM and commonly used prediction models were compared. Finally, the abilities of the OI variables and the OI variables processed by feature engineering (FEI) for different SOC prediction models were explored. The results show that 1) the A-GNN-LSTM that used OI as the input variable was the optimal prediction model (RMSE = 4.86 g kg&lt;sup&gt;−1&lt;/sup&gt;, R&lt;sup&gt;2&lt;/sup&gt; = 0.81, RPIQ = 2.46, and MAE = 3.78 g kg&lt;sup&gt;−1&lt;/sup&gt;) with the highest spatiotemporal transferability. 2) Compared to the temporal transferability of the GNN, the A-GNN-LSTM demonstrates superior temporal transferability (ΔR&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;T&lt;/sub&gt; = −0.10 vs. −0.07). Furthermore, compared to the spatial transferability of LSTM, the A-GNN-LSTM shows enhanced spatial transferability (ΔR&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;S&lt;/sub&gt; = −0.16 vs. −0.09). These findings strongly suggest that the fusion of geospatial context and temporally dependent information, extracted through the integration of GNN and LSTM models, effectively enhances the spatiotemporal transferability of the models. 3) By introducing the attention mechanism, the weights of different input variables could be calculated, increasing the physical interpretability of the deep learning model. The largest weight was assigned to climate data (39.55 %), and the smallest weight was assigned to vegetation (19.96 %). 4) Among the commonly used prediction models, the deep learning model had higher prediction accuracy (RMSE = 6.64 g kg&lt;sup&gt;−1&lt;/sup&gt;, R&lt;sup&gt;2&lt;/sup&gt; = 0.64, RPIQ = 1.78, and MAE = 4.78 g kg&lt;sup&gt;−1&lt;/sup&gt;) and spatial transferability (ΔRMSE&lt;sub&gt;S&lt;/sub&gt; = 1.","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 531-550"},"PeriodicalIF":10.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426558","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":"Automated localization of dike leakage outlets using UAV-borne thermography and YOLO-based object detectors","authors":"Renlian Zhou ,&nbsp;Monjee K. Almustafa ,&nbsp;Moncef L. Nehdi ,&nbsp;Huaizhi Su","doi":"10.1016/j.isprsjprs.2024.09.039","DOIUrl":"10.1016/j.isprsjprs.2024.09.039","url":null,"abstract":"<div><div>Leakage-induced soil erosion poses a major threat to dike failure, particularly during floods. Timely detection and notification of leakage outlets to dike management are crucial for ensuring dike safety. However, manual inspection, the current main approach for identifying leakage outlets, is costly, inefficient, and lacks spatial coverage. To achieve efficient and automatic localization of dike leakage outlets, an innovative strategy combining drones, infrared thermography, and deep learning is presented. Drones are employed for dikes’ surface sensing. Real-time images from these drones are sent to a server where well-trained detectors are deployed. Once a leakage outlet is detected, alarming information is remotely sent to dike managers. To realize this strategy, 4 thermal imagers were employed to image leaking outlets of several models and actual dikes. 9,231 hand-labeled thermal images with 13,387 leaking objects were selected for analysis. 19 detectors were trained using transfer learning. The best detector achieved a mean average precision of 95.8 % on the challenging test set. A full-scale embankment was constructed for leakage outlet detection tests. Various field tests confirmed the efficiency of the proposed leakage outlet localization method. In some tough conditions, the trained detector also evidently outperformed manual judgement. Results indicate that under typical circumstances, the localization error of the proposed method is within 5 m, demonstrating its practical reliability. Finally, the influencing factors and limits of the suggested strategy are thoroughly examined.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 551-573"},"PeriodicalIF":10.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426647","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}