Pub Date : 2023-05-01DOI: 10.14358/pers.22-00114r2
N. Ren, Xinyan Pang, Chang-qing Zhu, Shuitao Guo, Ying Xiong
To address the problem of weak robustness against geometric attacks of remote sensing images' digital watermarking, a robust watermark- ing algorithm based on template watermarking is proposed in this paper, which improves the robustness of digital watermarking against geometric attacks by constructing stable geometric attack invari- ant features. In this paper, the Discrete Fourier Transform domain template watermark is used as the invariant feature against geometric attacks, and the embedding of the cyclic watermark is used to improve the watermark robustness for recovering the watermark synchroniza- tion relationship. To achieve blind extraction of the watermark, a parameter extraction method based on noise extraction is designed. The experimental results demonstrate that the proposed method can effectively improve the robustness of digital watermarking of remote sensing images against geometric attacks. Meanwhile, it can also resist common image processing attacks and compound attacks.
{"title":"Blind and Robust Watermarking Algorithm for Remote Sensing Images Resistant to Geometric Attacks","authors":"N. Ren, Xinyan Pang, Chang-qing Zhu, Shuitao Guo, Ying Xiong","doi":"10.14358/pers.22-00114r2","DOIUrl":"https://doi.org/10.14358/pers.22-00114r2","url":null,"abstract":"To address the problem of weak robustness against geometric attacks of remote sensing images' digital watermarking, a robust watermark- ing algorithm based on template watermarking is proposed in this paper, which improves the robustness of digital watermarking against geometric attacks\u0000 by constructing stable geometric attack invari- ant features. In this paper, the Discrete Fourier Transform domain template watermark is used as the invariant feature against geometric attacks, and the embedding of the cyclic watermark is used to improve the watermark robustness for recovering\u0000 the watermark synchroniza- tion relationship. To achieve blind extraction of the watermark, a parameter extraction method based on noise extraction is designed. The experimental results demonstrate that the proposed method can effectively improve the robustness of digital watermarking of remote\u0000 sensing images against geometric attacks. Meanwhile, it can also resist common image processing attacks and compound attacks.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124046254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14358/pers.22-00111r2
Dan Li, Han-Zhen Wu, Yujian Wang, Xiaojun Li, Fanqiang Kong, Qiang Wang
Although most deep learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification, they are often limited by complex networks and require massive training samples in practical applications. Therefore, designing an efficient, lightweight model to obtain better classification results under small samples situations remains a challenging task. To alleviate this problem, a novel, lightweight parallel octave convolutional neural network (LPOCNN) for HSI classification is proposed in this paper. First, the HSI data is preprocessed to construct two three-dimensional (3D) patch cubes with different spatial and spectral scales for each central pixel, removing redundancy and focusing on extracting spatial features and spectral features, respectively. Next, two non- deep parallel branches are created for the two inputs, which design octave convolution rather than classical 3D convolution to facilitate light weighting of the model. Then two-dimensional convolutional neural network is used to extract deeper spectral-spatial features when fusing spectral-spatial features from different parallel layers. Moreover, the spectral-spatial attention is designed to promote the classification performance even further by adaptively adjusting the weights of different spectral-spatial features according to their contribution to classification. Experiments show that our suggested LPOCNN acquires a significant advantage on classification performance over other competitive methods under small sample situations.
{"title":"Lightweight Parallel Octave Convolutional Neural Network for Hyperspectral Image Classification","authors":"Dan Li, Han-Zhen Wu, Yujian Wang, Xiaojun Li, Fanqiang Kong, Qiang Wang","doi":"10.14358/pers.22-00111r2","DOIUrl":"https://doi.org/10.14358/pers.22-00111r2","url":null,"abstract":"Although most deep learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification, they are often limited by complex networks and require massive training samples in practical applications. Therefore, designing an efficient, lightweight model\u0000 to obtain better classification results under small samples situations remains a challenging task. To alleviate this problem, a novel, lightweight parallel octave convolutional neural network (LPOCNN) for HSI classification is proposed in this paper. First, the HSI data is preprocessed\u0000 to construct two three-dimensional (3D) patch cubes with different spatial and spectral scales for each central pixel, removing redundancy and focusing on extracting spatial features and spectral features, respectively. Next, two non- deep parallel branches are created for the two inputs,\u0000 which design octave convolution rather than classical 3D convolution to facilitate light weighting of the model. Then two-dimensional convolutional neural network is used to extract deeper spectral-spatial features when fusing spectral-spatial features from different parallel layers. Moreover,\u0000 the spectral-spatial attention is designed to promote the classification performance even further by adaptively adjusting the weights of different spectral-spatial features according to their contribution to classification. Experiments show that our suggested LPOCNN acquires a significant\u0000 advantage on classification performance over other competitive methods under small sample situations.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123081658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adnan Dashti, Faisal Al-Bous, Fahad Al Ajmi, Nasser Al Ajmi, N. Osman, Ramesh Mahishi V Murthy
{"title":"Mapping Kuwait Oil Company's Assets using Photogrammetry Techniques","authors":"Adnan Dashti, Faisal Al-Bous, Fahad Al Ajmi, Nasser Al Ajmi, N. Osman, Ramesh Mahishi V Murthy","doi":"10.14358/pers.89.4.197","DOIUrl":"https://doi.org/10.14358/pers.89.4.197","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131908955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14358/pers.22-00119r2
Ruifeng Ma, X. Ge, Qing Zhu, Xin Jia, Huiwei Jiang, Min Chen, Tao Liu
Highway markings (HMs) are representative elements of inventory digitalization in highway scenes. The accurate position, semantics, and maintenance information of HMs provide significant support for the intelligent management of highways. This article presents a robust and efficient approach for extracting, reconstructing, and degrading analyzing HMs in complex highway scenes. Compared with existing road marking extraction methods, not only can extract HMs in presence of wear and occlusion from point clouds, but we also perform a degradation analysis for HMs. First, the HMs candidate area is determined accurately by sophisticated image processing. Second, the prior knowledge of marking design rules and edge-based matching model that leverages the standard geometric template and radiometric appearance of HMs is used for accurately extracting and reconstructing solid lines and nonsolid markings of HMs, respectively. Finally, two degradation indicators are constructed to describe the completeness of the marking contour and consistency within the marking. Comprehensive experiments on two existing highways revealed that the proposed methods achieved an overall performance of 95.4% and 95.4% in the recall and 93.8% and 95.5% in the precision for solid line and nonsolid line markings, respectively, even with imperfect data. Meanwhile, a database can be established to facilitate agencies' efficient maintenance.
{"title":"Model-Driven Precise Degradation Analysis Method of Highway Marking Using Mobile Laser Scanning Point Clouds","authors":"Ruifeng Ma, X. Ge, Qing Zhu, Xin Jia, Huiwei Jiang, Min Chen, Tao Liu","doi":"10.14358/pers.22-00119r2","DOIUrl":"https://doi.org/10.14358/pers.22-00119r2","url":null,"abstract":"Highway markings (HMs) are representative elements of inventory digitalization in highway scenes. The accurate position, semantics, and maintenance information of HMs provide significant support for the intelligent management of highways. This article presents a robust and efficient\u0000 approach for extracting, reconstructing, and degrading analyzing HMs in complex highway scenes. Compared with existing road marking extraction methods, not only can extract HMs in presence of wear and occlusion from point clouds, but we also perform a degradation analysis for HMs. First, the\u0000 HMs candidate area is determined accurately by sophisticated image processing. Second, the prior knowledge of marking design rules and edge-based matching model that leverages the standard geometric template and radiometric appearance of HMs is used for accurately extracting and reconstructing\u0000 solid lines and nonsolid markings of HMs, respectively. Finally, two degradation indicators are constructed to describe the completeness of the marking contour and consistency within the marking. Comprehensive experiments on two existing highways revealed that the proposed methods achieved\u0000 an overall performance of 95.4% and 95.4% in the recall and 93.8% and 95.5% in the precision for solid line and nonsolid line markings, respectively, even with imperfect data. Meanwhile, a database can be established to facilitate agencies' efficient maintenance.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130458676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14358/pers.22-00092r2
Ayman M. Elameen, Shuanggen Jin, D. Olago
Terrestrial water storage (TWS) plays a vital role in climatological and hydrological processes. Most of the developed drought indices from the Gravity Recovery and Climate Experiment (GRACE) over Africa neglected the influencing roles of individual water storage components in calculating the drought index and thus may either underestimate or overestimate drought characteristics. In this paper, we proposed a Weighted Water Storage Deficit Index for drought assessment over the major river basins in Africa (i. e., Nile, Congo, Niger, Zambezi, and Orange) with accounting for the contribution of each TWS component on the drought signal. We coupled the GRACE data and WaterGAP Global Hydrology Model through utilizing the component contribution ratio as the weight. The results showed that water storage components demonstrated distinctly different contributions to TWS variability and thus drought signal response in onset and duration. The most severe droughts over the Nile, Congo, Niger, Zambezi, and Orange occurred in 2006, 2012, 2006, 2006, and 2003, respectively. The most prolonged drought of 84 months was observed over the Niger basin. This study suggests that considering the weight of individual components in the drought index provides more reasonable and realistic drought estimates over large basins in Africa from GRACE.
{"title":"Identification of Drought Events in Major Basins of Africa from GRACE Total Water Storage and Modeled Products","authors":"Ayman M. Elameen, Shuanggen Jin, D. Olago","doi":"10.14358/pers.22-00092r2","DOIUrl":"https://doi.org/10.14358/pers.22-00092r2","url":null,"abstract":"Terrestrial water storage (TWS) plays a vital role in climatological and hydrological processes. Most of the developed drought indices from the Gravity Recovery and Climate Experiment (GRACE) over Africa neglected the influencing roles of individual water storage components in calculating\u0000 the drought index and thus may either underestimate or overestimate drought characteristics. In this paper, we proposed a Weighted Water Storage Deficit Index for drought assessment over the major river basins in Africa (i. e., Nile, Congo, Niger, Zambezi, and Orange) with accounting for the\u0000 contribution of each TWS component on the drought signal. We coupled the GRACE data and WaterGAP Global Hydrology Model through utilizing the component contribution ratio as the weight. The results showed that water storage components demonstrated distinctly different contributions to TWS\u0000 variability and thus drought signal response in onset and duration. The most severe droughts over the Nile, Congo, Niger, Zambezi, and Orange occurred in 2006, 2012, 2006, 2006, and 2003, respectively. The most prolonged drought of 84 months was observed over the Niger basin. This study suggests\u0000 that considering the weight of individual components in the drought index provides more reasonable and realistic drought estimates over large basins in Africa from GRACE.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130804944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14358/pers.22-00051r2
Qiankun Fu, X. Tong, Shijie Liu, Z. Ye, Yanmin Jin, Hanyu Wang, Z. Hong
The precise geo-positioning of high-resolution satellite imagery (HRSI) without ground control points (GCPs) is an important and fundamental step in global mapping, three-dimensional modeling, and so on. In this paper, to improve the efficiency of large-scale bundle adjustment (BA), we propose a combined Preconditioned Conjugate Gradient (PCG) and Graphic Processing Unit (GPU) parallel computing approach for the BA of large-scale HRSI without GCPs. The proposed approach consists of three main components: 1) construction of a BA model without GCPs ; 2) reduction of memory consumption using the Compressed Sparse Row sparse matrix format; and 3) improvement of the computational efficiency by the use of the combined PCG and GPU parallel computing method. The experimental results showed that the proposed method: 1) consumes less memory consumption compared to the conventional full matrix format method; 2) demonstrates higher computational efficiency than the single-core, Ceres-solver and multi-core central processing unit computing methods, with 9.48, 6.82, and 3.05 times faster than the above three methods, respectively; 3) obtains comparable BA accuracy with the above three methods, with image residuals of about 0.9 pixels; and 4) is superior to the parallel bundle adjustment method in the reprojection error.
{"title":"GPU-Accelerated PCG Method for the Block Adjustment of Large-Scale High-Resolution Optical Satellite Imagery Without GCPs","authors":"Qiankun Fu, X. Tong, Shijie Liu, Z. Ye, Yanmin Jin, Hanyu Wang, Z. Hong","doi":"10.14358/pers.22-00051r2","DOIUrl":"https://doi.org/10.14358/pers.22-00051r2","url":null,"abstract":"The precise geo-positioning of high-resolution satellite imagery (HRSI) without ground control points (GCPs) is an important and fundamental step in global mapping, three-dimensional modeling, and so on. In this paper, to improve the efficiency of large-scale bundle adjustment (BA),\u0000 we propose a combined Preconditioned Conjugate Gradient (PCG) and Graphic Processing Unit (GPU) parallel computing approach for the BA of large-scale HRSI without GCPs. The proposed approach consists of three main components: 1) construction of a BA model without GCPs ; 2) reduction of memory\u0000 consumption using the Compressed Sparse Row sparse matrix format; and 3) improvement of the computational efficiency by the use of the combined PCG and GPU parallel computing method. The experimental results showed that the proposed method: 1) consumes less memory consumption compared to the\u0000 conventional full matrix format method; 2) demonstrates higher computational efficiency than the single-core, Ceres-solver and multi-core central processing unit computing methods, with 9.48, 6.82, and 3.05 times faster than the above three methods, respectively; 3) obtains comparable BA accuracy\u0000 with the above three methods, with image residuals of about 0.9 pixels; and 4) is superior to the parallel bundle adjustment method in the reprojection error.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133935796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"GIS Tips & Tricks — Buffers Everywhere but Where You Want Them?","authors":"Alma M. Karlin","doi":"10.14358/pers.89.4.203","DOIUrl":"https://doi.org/10.14358/pers.89.4.203","url":null,"abstract":"","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116619795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.14358/pers.22-00118r2
Tolga Bakirman, Bahadır Kulavuz, B. Bayram
Cultural heritage (CH) aims to create new strategies and policies for adapting to climate change. Additionally, the goals of sustainable development aim to protect, monitor, and preserve the world's CH and to take urgent action to combat climate change and its effects. Therefore, developing efficient and accurate techniques toward making CH climate neutral and more resilient is of vital importance. This study aims to provide a holistic solution to monitor and protect CHfrom climate change, natural hazards, and anthropogenic effects in a sustainable way. In our study, the efficiency of deep learning using low-cost unmanned aerial vehicles and camera images for the documentation and monitoring of CHis investigated. The dense extreme inception network for edge detection and richer convolu- tional feature architectures have been used for the first time in the literature to extract contours and cracks from CHstructures. As a result of the study, F1 scores of 61.38% and 61.50% for both architectures, respectively, were obtained. The results show that the proposed solution can aid in monitoring the protection of CHfrom climate change, natural disasters, and anthropogenic effects.
{"title":"Use of Artificial Intelligence Toward Climate-neutral Cultural Heritage","authors":"Tolga Bakirman, Bahadır Kulavuz, B. Bayram","doi":"10.14358/pers.22-00118r2","DOIUrl":"https://doi.org/10.14358/pers.22-00118r2","url":null,"abstract":"Cultural heritage (CH) aims to create new strategies and policies for adapting to climate change. Additionally, the goals of sustainable development aim to protect, monitor, and preserve the world's CH and to take urgent action to combat climate change and its effects. Therefore, developing\u0000 efficient and accurate techniques toward making CH climate neutral and more resilient is of vital importance. This study aims to provide a holistic solution to monitor and protect CHfrom climate change, natural hazards, and anthropogenic effects in a sustainable way. In our study, the efficiency\u0000 of deep learning using low-cost unmanned aerial vehicles and camera images for the documentation and monitoring of CHis investigated. The dense extreme inception network for edge detection and richer convolu- tional feature architectures have been used for the first time in the literature\u0000 to extract contours and cracks from CHstructures. As a result of the study, F1 scores of 61.38% and 61.50% for both architectures, respectively, were obtained. The results show that the proposed solution can aid in monitoring the protection of CHfrom climate change, natural disasters, and\u0000 anthropogenic effects.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123689886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.14358/pers.22-00109r2
Jian Wu, Shifeng Fu, Peng Chen, Qing-hui Chen, Xiang Pan
The unmanned aerial vehicle (UAV) remote sensing is of small volume, low cost, fine timeliness, and high spatial resolution, and has the special advantage on island surveying. Focus on the inaccurate elevation of non-ground point cloud without lidar device, this study explored a methodology for island three-dimensional (3D) mapping and modelling based on spatial point clouds optimization with a K-Nearest Neighbors Adaptive Inverse Distance Weighted (K-AIDW) interpolation algorithm. By classifying the UAV point clouds into ground, vegatetation, and structure, the K-AIDW algorithm was applied to optimize the elevations of non-ground point clouds (vegetation and structure) to recalculate Z values. The aerophotogrammetry result was generated based on the optimized spatial point clouds. Finally, the 3D model of Dongluo Island was reconstructed and rendered in Metashape. The accuracy evaluation result shows that the max-errors of ground control points (–0.0154 in X, 0.0305 in Y, and 0.0133 in Z) and the checkpoints (–0.091 in X, –0.176 in Y, and 0.338 in Z) can meet the error-tolerance requirements of the corresponding terrain on the 1:500 scale set by the national standard of GB/T 23236-2009 in China. It is found that the K-AIDW algorithm displayed the best Z accuracy (root-mean-square error of 0.2538) compared with IDW (0.3668) and no-optimized (1.6012), proving it is an effective methodology for improving 3D-modelling accuracy of island.
{"title":"Validation of Island 3D-mapping Based on UAV Spatial Point Cloud Optimization: a Case Study in Dongluo Island of China","authors":"Jian Wu, Shifeng Fu, Peng Chen, Qing-hui Chen, Xiang Pan","doi":"10.14358/pers.22-00109r2","DOIUrl":"https://doi.org/10.14358/pers.22-00109r2","url":null,"abstract":"The unmanned aerial vehicle (UAV) remote sensing is of small volume, low cost, fine timeliness, and high spatial resolution, and has the special advantage on island surveying. Focus on the inaccurate elevation of non-ground point cloud without lidar device, this study explored a methodology\u0000 for island three-dimensional (3D) mapping and modelling based on spatial point clouds optimization with a K-Nearest Neighbors Adaptive Inverse Distance Weighted (K-AIDW) interpolation algorithm. By classifying the UAV point clouds into ground, vegatetation, and structure, the K-AIDW algorithm\u0000 was applied to optimize the elevations of non-ground point clouds (vegetation and structure) to recalculate Z values. The aerophotogrammetry result was generated based on the optimized spatial point clouds. Finally, the 3D model of Dongluo Island was reconstructed and rendered in Metashape.\u0000 The accuracy evaluation result shows that the max-errors of ground control points (–0.0154 in X, 0.0305 in Y, and 0.0133 in Z) and the checkpoints (–0.091 in X, –0.176 in Y, and 0.338 in Z) can meet the error-tolerance requirements of the corresponding terrain on the 1:500\u0000 scale set by the national standard of GB/T 23236-2009 in China. It is found that the K-AIDW algorithm displayed the best Z accuracy (root-mean-square error of 0.2538) compared with IDW (0.3668) and no-optimized (1.6012), proving it is an effective methodology for improving 3D-modelling accuracy\u0000 of island.","PeriodicalId":211256,"journal":{"name":"Photogrammetric Engineering & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133786681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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