The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences最新文献_第3页

POSER: an oPen sOurce Simulation platform for tEaching and tRaining underwater photogrammetry POSER：用于水下摄影测量的遥感模拟平台

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-265-2024

F. Menna, Scott McAvoy, E. Nocerino, B. Tanduo, Louise Giuseffi, A. Calantropio, F. Chiabrando, L. Teppati Losè, A. Lingua, Stuart Sandin, Clinton Edwards, Brian Zgliczynski, D. Rissolo, F. Kuester

{"title":"POSER: an oPen sOurce Simulation platform for tEaching and tRaining underwater photogrammetry","authors":"F. Menna, Scott McAvoy, E. Nocerino, B. Tanduo, Louise Giuseffi, A. Calantropio, F. Chiabrando, L. Teppati Losè, A. Lingua, Stuart Sandin, Clinton Edwards, Brian Zgliczynski, D. Rissolo, F. Kuester","doi":"10.5194/isprs-archives-xlviii-2-2024-265-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-265-2024","url":null,"abstract":"Abstract. Underwater photogrammetry presents unique challenges due to the optical properties of water that, if not correctly taken into account, might affect the quality of the survey and the related 2D and 3D products. It is recognized nowadays the importance to train newcomers to underwater surveying, and extend and consolidate the knowledge of best practices for underwater data acquisition. Starting from this consideration, we propose the development of POSER, a 3D simulation framework designed to facilitate the teaching of underwater imaging principles. The project, an ISPRS Educational and Capacity Building Initiative, is built upon the open-source platform Blender, incorporating realistic modelling of the physical properties of water, including light refraction, scattering, and absorption phenomena, to simulate underwater surveying conditions. We foster a learning-by-doing approach, providing users with ready-to-use application scenarios inspired by real-life case studies. They will cover a range of application fields, from marine ecology to archaeology and subsea metrology, and allow users to address the complexities of underwater surveying practices. This paper introduces POSER to the community, presenting its educational vocation and describing its constituent components.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"20 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359303","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}

引用次数: 1

Automatic Vectorization of Power Lines from Airborne Lidar Point Clouds 从机载激光雷达点云自动矢量化电力线

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-225-2024

E. Maset, Andrea Fusiello

引用次数: 0

Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation 利用三维高斯拼接技术进行大规模三维地形重建以实现可视化和仿真

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-49-2024

Meida Chen, Devashish Lal, Zifan Yu, Jiuyi Xu, Andrew Feng, Suya You, Abdul Nurunnabi, Yangming Shi

{"title":"Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation","authors":"Meida Chen, Devashish Lal, Zifan Yu, Jiuyi Xu, Andrew Feng, Suya You, Abdul Nurunnabi, Yangming Shi","doi":"10.5194/isprs-archives-xlviii-2-2024-49-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-49-2024","url":null,"abstract":"Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"30 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357509","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}

引用次数: 0

Evaluating Linear Coral Growth Estimation Using Photogrammetry and Alternative Point Cloud Comparison Methods 利用摄影测量和其他点云比较方法评估线性珊瑚生长估算法

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-121-2024

P. Helmholz, Tahlia Bassett, Liam Boyle, Nicola Browne, I. Parnum, Molly Moustaka, Richard Evans

{"title":"Evaluating Linear Coral Growth Estimation Using Photogrammetry and Alternative Point Cloud Comparison Methods","authors":"P. Helmholz, Tahlia Bassett, Liam Boyle, Nicola Browne, I. Parnum, Molly Moustaka, Richard Evans","doi":"10.5194/isprs-archives-xlviii-2-2024-121-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-121-2024","url":null,"abstract":"Abstract. Corals are critical reef-building organisms, providing essential habitat and ecosystem services. Tracking coral growth over time indicates coral reef health, which can be measured using various established techniques. Several coral growth-related studies have successfully applied photogrammetry to a particular coral of various types. While the focus of previous work was on standardised data processing and, to a certain degree, on the assessment of different point cloud comparison methods (Lange et al. 2022), little attention has been given to the impact of camera calibration. This study measured the annual linear extension of five Acropora spp. colonies using photogrammetry and evaluated all stages of imagery processing. A high focus was given to the analysis of the camera calibration method and the validation of camera parameters derived using an in-situ calibration of coral images with scale bars placed in the camera's field of view. We demonstrate that this method is as reliable as the calibration using a calibration frame. This study also examined the impact of the different point cloud comparison methods for Acropora spp. More specifically, the derived point clouds are compared by applying the point-to-point and point-to-model methods and manually selecting 12 coral branch tips. Histograms derived from the comparison methods were analysed and deemed a suitable and efficient alternative approach for measuring the maximum growth rate of mature colonies over shorter time periods (1 year or less).\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356784","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}

引用次数: 0

Integrating Crowd-sourced Annotations of Tree Crowns using Markov Random Field and Multispectral Information 利用马尔可夫随机场和多光谱信息整合树冠的众包注释

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-257-2024

Qipeng Mei, Janik Steier, D. Iwaszczuk

{"title":"Integrating Crowd-sourced Annotations of Tree Crowns using Markov Random Field and Multispectral Information","authors":"Qipeng Mei, Janik Steier, D. Iwaszczuk","doi":"10.5194/isprs-archives-xlviii-2-2024-257-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-257-2024","url":null,"abstract":"Abstract. Benefiting from advancements in algorithms and computing capabilities, supervised deep learning models offer significant advantages in accurately mapping individual tree canopy cover, which is a fundamental component of forestry management. In contrast to traditional field measurement methods, deep learning models leveraging remote sensing data circumvent access limitations and are more cost-effective. However, the efficiency of models depends on the accuracy of the tree crown annotations, which are often obtained through manual labeling. The intricate features of the tree crown, characterized by irregular contours, overlapping foliage, and frequent shadowing, pose a challenge for annotators. Therefore, this study explores a novel approach that integrates the annotations of multiple annotators for the same region of interest. It further refines the labels by leveraging information extracted from multi-spectral aerial images. This approach aims to reduce annotation inaccuracies caused by personal preference and bias and obtain a more balanced integrated annotation.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"51 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360293","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}

引用次数: 0

Automated Registration of Full Moon Remote Sensing Images Based on Triangulated Network Constraints 基于三角网约束的满月遥感图像自动配准技术

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-89-2024

Huibin Ge, Yu Geng, Xiaojuan Ba, Yuxiang Wang, Jingguo Lv

{"title":"Automated Registration of Full Moon Remote Sensing Images Based on Triangulated Network Constraints","authors":"Huibin Ge, Yu Geng, Xiaojuan Ba, Yuxiang Wang, Jingguo Lv","doi":"10.5194/isprs-archives-xlviii-2-2024-89-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-89-2024","url":null,"abstract":"Abstract. The registration of full-moon remote sensing images constitutes a pivotal stage in the fusion analysis of multiple lunar remote sensing datasets. Addressing prevailing issues in automatic registration, such as the broad width of full-moon data, significant internal distortion, and texture distortion in high-latitude regions, this paper proposes a method for automatic matching and correction based on triangulation constraints. The approach employs a matching strategy progressing from coarse to fine and from sparse to dense. It optimizes and combines multiple existing matching algorithms, enhances the extraction of initial network points, constructs irregular triangulation networks using these points, conducts dense matching with each triangulation network as a basic unit, and introduces a geometric correction method based on triangulation network + grid (TIN + GRID) for the registration of full-moon data. For the matching of full-moon remote sensing images in high-latitude regions, a novel approach involving memory projection forward transformation-matching-projection inverse transformation is adopted. Through registration experiments with full-moon image data and an analysis of registration accuracy at different latitudes, the average mean square error is found to be less than 2 pixels. These results signify the efficacy of the proposed method in effectively addressing the automatic registration challenges encountered in full-moon remote sensing images.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"20 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357190","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}

引用次数: 0

Integrating Widespread Coral Reef Monitoring Tools for Managing both Area and Point Annotations 整合广泛的珊瑚礁监测工具，管理区域和点注释

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-327-2024

G. Pavoni, Jordan Pierce, Clinton B. Edwards, M. Corsini, Vid Petrovic, Paolo Cignoni

{"title":"Integrating Widespread Coral Reef Monitoring Tools for Managing both Area and Point Annotations","authors":"G. Pavoni, Jordan Pierce, Clinton B. Edwards, M. Corsini, Vid Petrovic, Paolo Cignoni","doi":"10.5194/isprs-archives-xlviii-2-2024-327-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-327-2024","url":null,"abstract":"Abstract. Large-area image acquisition techniques are essential in underwater investigations: high-resolution 3D image-based reconstructions have improved coral reef monitoring by enabling novel seascape ecological analysis. Artificial intelligence (AI) offers methods for significantly accelerating image data interpretation, such as automatically recognizing, enumerating, and measuring organisms. However, the rapid proliferation of these technological achievements has led to a relative lack of standardization of methods. Remarkably, there are notable differences in procedures for generating human and AI annotations, and there is also a scarcity of publicly available datasets and shared machine-learning models. The lack of standard procedures makes it challenging to compare and reproduce scientific findings. One way to overcome this problem is to make the most used platforms by coral reef scientists interoperable so that the analyses can all be exported into a common format. This paper introduces functionality to promote interoperability between three popular open-source software tools dedicated to the digital study of coral reefs: TagLab, CoralNet, and Viscore. As users of each platform may have different analysis pipelines, we discuss several workflows for managing and processing point and area annotations, improving collaboration among these tools. Our work sets the foundation for a more seamless ecosystem that maintains the established investigation procedures of various laboratories but allows for easier result sharing.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359894","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}

引用次数: 0

An End-to-End Geometric Characterization-aware Semantic Instance Segmentation Network for ALS Point Clouds 面向 ALS 点云的端到端几何特征感知语义实例分割网络

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-435-2024

Jinhong Wang, W. Yao

{"title":"An End-to-End Geometric Characterization-aware Semantic Instance Segmentation Network for ALS Point Clouds","authors":"Jinhong Wang, W. Yao","doi":"10.5194/isprs-archives-xlviii-2-2024-435-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-435-2024","url":null,"abstract":"Abstract. Semantic instance segmentation from scenes, serving as a crucial role for 3D modelling and scene understanding. Conducting semantic segmentation before grouping instances is adopted by the existing state-of-the-art methods. However, without additional refinement, semantic errors will fully propagate into the grouping stage, resulting in low overlap with the ground truth instance. Furthermore, the proposed methods focused on indoor level scenes, which are limited when directly applied to large-scale outdoor Airborne Laser Scanning (ALS) point clouds. Numerous instances, significant object density and scale variations make ALS point clouds distinct from indoor data. In order to address the problems, we proposed a geometric characterization-aware semantic instance segmentation network, which utilized both semantic and objectness score to select potential points for grouping. And in point cloud feature learning stage, hand-craft geometry features are taken as input for geometric characterization awareness. Moreover, to address errors propagated from previous modules after grouping, we have additionally designed a per-instance refinement module. To assess semantic instance segmentation, we conducted experiments on an open-source dataset. Additionally, we performed semantic segmentation experiments to evaluate the performance of our proposed point cloud feature learning method.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"39 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355539","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}

引用次数: 0

A Multimodal Approach to Rapidly Documenting and Visualizing Archaeological Caves in Quintana Roo, Mexico 快速记录和展示墨西哥金塔纳罗奥州考古洞穴的多模式方法

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-349-2024

D. Rissolo, Scott McAvoy, Helena Barba Meinecke, H. Moyes, Samuel Meacham, Julien Fortin, Fred Devos, F. Kuester

{"title":"A Multimodal Approach to Rapidly Documenting and Visualizing Archaeological Caves in Quintana Roo, Mexico","authors":"D. Rissolo, Scott McAvoy, Helena Barba Meinecke, H. Moyes, Samuel Meacham, Julien Fortin, Fred Devos, F. Kuester","doi":"10.5194/isprs-archives-xlviii-2-2024-349-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-349-2024","url":null,"abstract":"Abstract. Clearing and construction activities related to the Maya Train (Tren Maya) project resulted in potential and inevitable impacts to archaeological caves sites in largely undeveloped areas of Quintana Roo. An effort coordinated by Mexico’s National Institute of Anthropology and History (INAH) involved accelerated digital documentation of two caves – via SLAM-enabled mobile LiDAR scanning and targeted photogrammetry – to facilitate prompt visualization and evaluation of terrestrial and subterranean geospatial relationships. Mobile LiDAR is well suited to the challenges of capturing the complex, multilevel morphology of caves and was readily deployed across and through priority environments. Specific archaeological features – such as ancient Maya rock art and masonry shrines – were documented via photogrammetry, and the resulting higher-resolution models co-referenced with the georeferenced mobile LiDAR-generated point clouds of each cave and the surrounding topographic context. This integrative approach contributed to a more informed decision-making process, with respect to conservation and construction, and provided baseline data for future monitoring of the affected cave sites.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"37 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360167","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}

引用次数: 0

Multimedia Photogrammetry for Automated 3D Monitoring in Archaeological Waterlogged Wood Conservation 多媒体摄影测量用于考古涝害木材保护中的自动三维监测

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-06-11 DOI: 10.5194/isprs-archives-xlviii-2-2024-355-2024

R. Rofallski, A. Colson, T. Luhmann

{"title":"Multimedia Photogrammetry for Automated 3D Monitoring in Archaeological Waterlogged Wood Conservation","authors":"R. Rofallski, A. Colson, T. Luhmann","doi":"10.5194/isprs-archives-xlviii-2-2024-355-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-355-2024","url":null,"abstract":"Abstract. This study addresses the challenges inherent in preserving archaeological waterlogged wood, which is prone to deformation and decay if not stabilized immediately after recovery. Conventional preservation methods, such as impregnation with polyethylene glycol (PEG) solutions, often result in undesirable dimensional changes. To obtain exact spatio-temporal information on the deformations during the conservation process, a photogrammetric monitoring system, utilizing a stereo camera facing from air into the liquid, attached to an automated biaxial measurement unit is proposed. Special target heads were developed and attached to the wood to provide deformation points. Refraction correction was applied to the imaging model by ray tracing, and indirect flat lighting was used to mitigate turbidity. The system observed logs from a wooden track from the first century, subject to conservation. Subject of investigation were the influence of refraction negligence and scale definition in a bundle geometry, similar to bathymetric aerial setups. Results show that refraction correction is imperative for good results. Furthermore, scale definition with highly accurately determined scale bars and inclusion of relative orientation constraints provide further accuracy improvements.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"44 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355022","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}

引用次数: 0