Pub Date : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-99-2024
Tao Huang, Hongbo Pan, Nanxi Zhou
Abstract. Due to the influence of image differences and matching methods, geometric calibration of remote sensing images often results in the extraction of control points with inevitable outliers. Moreover, it is susceptible to limitations imposed by locally constrained outlier rejection methods, making it challenging to automatically remove relatively small gross errors. This paper introduces an adaptive parameter local consistency automatic outlier removal algorithm, referred to as APLC. Initially, we construct k-nearest neighbors for each pair of matching points, deriving distance and topological uncertainty based on the accuracy of point matching. Subsequently, we conduct cross-validation on the uncertainty between the two pairs of vectors formed by points within the neighborhood, aiming for parameter adaptation. Finally, a cost-defined function is introduced to assess the consistency of local structures. Through a two-stage outlier removal strategy, matching points that do not maintain local structural consistency are eliminated. To assess the effectiveness of the proposed algorithm, we conduct experimental comparisons using region-based initial matching results from the FY-3D remote sensing dataset, demonstrating its superiority compared to three state-of-the-art methods.
摘要由于图像差异和匹配方法的影响，遥感图像的几何校准往往会导致控制点的提取不可避免地出现离群值。此外，它还容易受到局部约束离群值剔除方法的限制，使得自动剔除相对较小的粗大误差具有挑战性。本文介绍了一种自适应参数局部一致性自动离群点剔除算法，简称 APLC。首先，我们为每对匹配点构建 k 个近邻，根据点匹配的准确性推导出距离和拓扑不确定性。随后，我们对邻域内的点所形成的两对向量之间的不确定性进行交叉验证，以达到参数调整的目的。最后，我们引入了一个成本定义函数来评估局部结构的一致性。通过两阶段离群点去除策略，剔除不能保持局部结构一致性的匹配点。为了评估所提算法的有效性，我们使用 FY-3D 遥感数据集的基于区域的初始匹配结果进行了实验比较，结果表明该算法优于三种最先进的方法。
{"title":"Adaptive parameter local consistency automatic outlier removal algorithm for area-based matching","authors":"Tao Huang, Hongbo Pan, Nanxi Zhou","doi":"10.5194/isprs-annals-x-1-2024-99-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-99-2024","url":null,"abstract":"Abstract. Due to the influence of image differences and matching methods, geometric calibration of remote sensing images often results in the extraction of control points with inevitable outliers. Moreover, it is susceptible to limitations imposed by locally constrained outlier rejection methods, making it challenging to automatically remove relatively small gross errors. This paper introduces an adaptive parameter local consistency automatic outlier removal algorithm, referred to as APLC. Initially, we construct k-nearest neighbors for each pair of matching points, deriving distance and topological uncertainty based on the accuracy of point matching. Subsequently, we conduct cross-validation on the uncertainty between the two pairs of vectors formed by points within the neighborhood, aiming for parameter adaptation. Finally, a cost-defined function is introduced to assess the consistency of local structures. Through a two-stage outlier removal strategy, matching points that do not maintain local structural consistency are eliminated. To assess the effectiveness of the proposed algorithm, we conduct experimental comparisons using region-based initial matching results from the FY-3D remote sensing dataset, demonstrating its superiority compared to three state-of-the-art methods.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995481","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-197-2024
Qinghong Sheng, Yuejie Zhang, Kerui Li, Xiao Ling, Jun Li
Abstract. LST (Land Surface Temperature) is a significant parameter that represents the ground energy balance and plays a crucial role in understanding climate change. The LST of the Tibetan Plateau (TP) has a direct influence on the climate and environmental changes of the TP, and it also has a significant impact on global climate and atmospheric circulation. Although there are various factors that drive the spatial and temporal distribution of LST on the TP, the primary driving forces and its seasonal variations of LST are not yet well understood. The research focuses specifically on the TP region, selecting three types of LST data, using geodetector model, to analyze the driving factors affecting the spatial pattern of LST in different seasons. The results indicate that the three factors, Air Temperature (AT), Elevation (Ele), and Permafrost Thermal Stability (PTS), have a significant influence on LST throughout all seasons, whereas other variables demonstrate varying contributions to LST depending on the season. This study contributes to the understanding of the spatial variability of surface thermal conditions and the intricate relationships between their driving factors. It also emphasizes the potential changes in these relationships throughout the year.
{"title":"Exploring the Seasonal Comparison of Land Surface Temperature Dominant Factors in the Tibetan Plateau","authors":"Qinghong Sheng, Yuejie Zhang, Kerui Li, Xiao Ling, Jun Li","doi":"10.5194/isprs-annals-x-1-2024-197-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-197-2024","url":null,"abstract":"Abstract. LST (Land Surface Temperature) is a significant parameter that represents the ground energy balance and plays a crucial role in understanding climate change. The LST of the Tibetan Plateau (TP) has a direct influence on the climate and environmental changes of the TP, and it also has a significant impact on global climate and atmospheric circulation. Although there are various factors that drive the spatial and temporal distribution of LST on the TP, the primary driving forces and its seasonal variations of LST are not yet well understood. The research focuses specifically on the TP region, selecting three types of LST data, using geodetector model, to analyze the driving factors affecting the spatial pattern of LST in different seasons. The results indicate that the three factors, Air Temperature (AT), Elevation (Ele), and Permafrost Thermal Stability (PTS), have a significant influence on LST throughout all seasons, whereas other variables demonstrate varying contributions to LST depending on the season. This study contributes to the understanding of the spatial variability of surface thermal conditions and the intricate relationships between their driving factors. It also emphasizes the potential changes in these relationships throughout the year.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994933","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-191-2024
Lei Qin, Yawen Liu, Xinbo Zhao, Yansong Duan
Abstract. As a clean energy source, natural gas is widely used and primarily transported through long-distance pipelines. Regular maintenance and inspection of long-distance gas pipelines are crucial tasks. Due to the extensive coverage and distance of these pipelines, the workload is enormous. It is necessary to first identify areas of change, which can be carried out using multiple sets of orthophotos produced by unmanned aerial vehicles (UAVs). However, UAV images have small footprints and significant geometric distortions, requiring a large number of ground control points (GCPs) for accurate positioning. Measuring these points in the field is challenging and time-consuming, becoming a key factor limiting the rapid production of orthophotos. To overcome this challenge, this paper introduces the "cloud control" photogrammetry technology to achieve fully automatic updates of orthophotos around long-distance pipelines, providing foundational data for the maintenance and inspection of these gas pipelines. This method replaces GCPs with images containing known orientation parameters, serving as control information. By matching tie points between new and old images, the "cloud control points" are transferred to the new images, enabling the image registration and production of orthophotos. The experiments conducted on the Fumin and Zhaotong segments of a long-distance gas pipeline in Yunnan Province demonstrate that, for UAV images with a ground resolution of 0.05 meters, using the "cloud control" method achieves a planar accuracy of 0.05 meters and an elevation accuracy of 0.07 meters. These results are comparable to the accuracy obtained by orienting the results using GCPs.
{"title":"Updating Orthophotos around Gas Pipelines based on \"Cloud Control\" Photogrammetry","authors":"Lei Qin, Yawen Liu, Xinbo Zhao, Yansong Duan","doi":"10.5194/isprs-annals-x-1-2024-191-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-191-2024","url":null,"abstract":"Abstract. As a clean energy source, natural gas is widely used and primarily transported through long-distance pipelines. Regular maintenance and inspection of long-distance gas pipelines are crucial tasks. Due to the extensive coverage and distance of these pipelines, the workload is enormous. It is necessary to first identify areas of change, which can be carried out using multiple sets of orthophotos produced by unmanned aerial vehicles (UAVs). However, UAV images have small footprints and significant geometric distortions, requiring a large number of ground control points (GCPs) for accurate positioning. Measuring these points in the field is challenging and time-consuming, becoming a key factor limiting the rapid production of orthophotos. To overcome this challenge, this paper introduces the \"cloud control\" photogrammetry technology to achieve fully automatic updates of orthophotos around long-distance pipelines, providing foundational data for the maintenance and inspection of these gas pipelines. This method replaces GCPs with images containing known orientation parameters, serving as control information. By matching tie points between new and old images, the \"cloud control points\" are transferred to the new images, enabling the image registration and production of orthophotos. The experiments conducted on the Fumin and Zhaotong segments of a long-distance gas pipeline in Yunnan Province demonstrate that, for UAV images with a ground resolution of 0.05 meters, using the \"cloud control\" method achieves a planar accuracy of 0.05 meters and an elevation accuracy of 0.07 meters. These results are comparable to the accuracy obtained by orienting the results using GCPs.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995913","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-267-2024
Hao Wu, Canhai Li, Yongchang Li
Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification. This article utilizes an encoding decoding structure to simultaneously extract deep and shallow features of images. A REGCS convolution module was constructed using the idea of group convolution to extract spectral and spatial features of images. We compared the Salinas Valley dataset and MUUFL dataset with various classification algorithms. The experimental results show that compared with other classification models, the RESSU model has achieved stable and excellent results in hyperspectral image classification experiments. Among them, in the classification experiment of the Salinas Valley dataset, the accuracy of single class classification reached over 92%. In the effectiveness analysis experiment, we calculated different model parameter quantities to verify the performance of our method, and ultimately achieved good results.
{"title":"Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network","authors":"Hao Wu, Canhai Li, Yongchang Li","doi":"10.5194/isprs-annals-x-1-2024-267-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-267-2024","url":null,"abstract":"Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification. This article utilizes an encoding decoding structure to simultaneously extract deep and shallow features of images. A REGCS convolution module was constructed using the idea of group convolution to extract spectral and spatial features of images. We compared the Salinas Valley dataset and MUUFL dataset with various classification algorithms. The experimental results show that compared with other classification models, the RESSU model has achieved stable and excellent results in hyperspectral image classification experiments. Among them, in the classification experiment of the Salinas Valley dataset, the accuracy of single class classification reached over 92%. In the effectiveness analysis experiment, we calculated different model parameter quantities to verify the performance of our method, and ultimately achieved good results.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995194","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-83-2024
Dan Gao, Xiaofang Wu, Zhijin Wen, Yue Xu, Zhengchao Chen
Abstract. The study of few-shot SAR image generation is an effective way to expand the SAR dataset, which not only provides diversified data support for SAR target classification, but also provides a high-fidelity false image template for SAR deceptive jamming. In this paper, we have constructed a multi-frequency and multi-target type SAR vehicle imagery dataset that encompasses frequencies such as X, Ka, P, and S bands. The vehicle types are coaster, suv and cabin. Subsequently, we utilized various Generative Adversarial Networks for image generation from the SAR vehicle dataset. The experimental result indicates that the images generated by the DCGAN and the LSGAN models are of superior quality. Furthermore, we employed different recognition networks to evaluate the classification accuracy of the generated images. Of all the frequency bands, the Ka band generated images achieved the highest recognition rate, with an accuracy of up to 99%. Under conditions of a limited number of samples, the LSGAN model performed the best, reaching a classification recognition rate of 71.48% with a dataset of only 20 samples. Finally, we use a conditional network generation model to generate conditions based on target categories and frequency bands, providing high fidelity samples for SAR deception jamming.
摘要少发 SAR 图像生成研究是拓展 SAR 数据集的有效途径，不仅能为 SAR 目标分类提供多样化的数据支持，还能为 SAR 欺骗性干扰提供高保真的虚假图像模板。本文构建了一个多频率、多目标类型的 SAR 车辆图像数据集，涵盖 X、Ka、P 和 S 波段。车辆类型包括飞车、越野车和客舱。随后，我们利用各种生成对抗网络从合成孔径雷达车辆数据集生成图像。实验结果表明，DCGAN 和 LSGAN 模型生成的图像质量上乘。此外，我们还采用了不同的识别网络来评估生成图像的分类准确性。在所有频段中，Ka 频段生成的图像识别率最高，准确率高达 99%。在样本数量有限的条件下，LSGAN 模型表现最佳，在只有 20 个样本的数据集上，分类识别率达到 71.48%。最后，我们使用条件网络生成模型，根据目标类别和频段生成条件，为合成孔径雷达欺骗干扰提供高保真样本。
{"title":"Few-shot SAR vehicle target augmentation based on generative adversarial networks","authors":"Dan Gao, Xiaofang Wu, Zhijin Wen, Yue Xu, Zhengchao Chen","doi":"10.5194/isprs-annals-x-1-2024-83-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-83-2024","url":null,"abstract":"Abstract. The study of few-shot SAR image generation is an effective way to expand the SAR dataset, which not only provides diversified data support for SAR target classification, but also provides a high-fidelity false image template for SAR deceptive jamming. In this paper, we have constructed a multi-frequency and multi-target type SAR vehicle imagery dataset that encompasses frequencies such as X, Ka, P, and S bands. The vehicle types are coaster, suv and cabin. Subsequently, we utilized various Generative Adversarial Networks for image generation from the SAR vehicle dataset. The experimental result indicates that the images generated by the DCGAN and the LSGAN models are of superior quality. Furthermore, we employed different recognition networks to evaluate the classification accuracy of the generated images. Of all the frequency bands, the Ka band generated images achieved the highest recognition rate, with an accuracy of up to 99%. Under conditions of a limited number of samples, the LSGAN model performed the best, reaching a classification recognition rate of 71.48% with a dataset of only 20 samples. Finally, we use a conditional network generation model to generate conditions based on target categories and frequency bands, providing high fidelity samples for SAR deception jamming.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995028","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-91-2024
Haoran Guo, Weijun Li, J. Dong, Yansong Duan
Abstract. A Digital Surface Model (DSM) is a crucial spatial geographic information data used to describe the shape of the earth’s surface in Geographic Information Systems (GIS). DSM is the core data used in terrain analysis in GIS. A regular grid DSM is generally generated by interpolating a large number of discrete point clouds. This paper proposes a method of using a hierarchical weighted strategy to fit a regular grid DSM with discrete points. This method uses a pyramid hierarchical strategy to refine the target regular grid from one grid with finer parameters of 3*3, until the nth level (the interval of the grid is equal to the expected interval), and then gradually places the discrete point cloud into the corresponding grid by weighted averaging, and uses the result of this level as the initial value of the next level. This algorithm can avoid the problem of low efficiency in retrieving a large number of discrete point clouds, and the indirect interpolation method not considering the contribution of distant neighboring point clouds. The operation of point cloud data is a stream operation, which does not require consideration of the topological information of point clouds, and has simple operation and no additional memory consumption. It is especially suitable for the production of regular grid DSM with massive point clouds. To verify the effectiveness of this method, the article selected six typical terrain data such as high mountains, mountains, hills, plains, urban areas, and lakes for experiments. The results show that compared with the construct-TIN method for producing DSM, this method has very good processing accuracy and processing efficiency.
{"title":"A method for hierarchical weighted fitting of regular grid DSM with discrete points","authors":"Haoran Guo, Weijun Li, J. Dong, Yansong Duan","doi":"10.5194/isprs-annals-x-1-2024-91-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-91-2024","url":null,"abstract":"Abstract. A Digital Surface Model (DSM) is a crucial spatial geographic information data used to describe the shape of the earth’s surface in Geographic Information Systems (GIS). DSM is the core data used in terrain analysis in GIS. A regular grid DSM is generally generated by interpolating a large number of discrete point clouds. This paper proposes a method of using a hierarchical weighted strategy to fit a regular grid DSM with discrete points. This method uses a pyramid hierarchical strategy to refine the target regular grid from one grid with finer parameters of 3*3, until the nth level (the interval of the grid is equal to the expected interval), and then gradually places the discrete point cloud into the corresponding grid by weighted averaging, and uses the result of this level as the initial value of the next level. This algorithm can avoid the problem of low efficiency in retrieving a large number of discrete point clouds, and the indirect interpolation method not considering the contribution of distant neighboring point clouds. The operation of point cloud data is a stream operation, which does not require consideration of the topological information of point clouds, and has simple operation and no additional memory consumption. It is especially suitable for the production of regular grid DSM with massive point clouds. To verify the effectiveness of this method, the article selected six typical terrain data such as high mountains, mountains, hills, plains, urban areas, and lakes for experiments. The results show that compared with the construct-TIN method for producing DSM, this method has very good processing accuracy and processing efficiency.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994981","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 : 2024-05-09DOI: 10.5194/isprs-annals-x-1-2024-183-2024
José M. Pacheco, A. Tommaselli
Abstract. Omnidirectional images are increasingly being used in various areas, such as urban mapping, virtual reality, agriculture, and robotics. These images can be generated by different acquisition systems, including multi-camera systems, which can acquire higher-resolution images. Stitching techniques are often used and can be suitable for non-metric applications, but rigorous photogrammetric processing is recommended when having more accurate requirements. The main challenges related to this kind of product are the system calibration and the generation of the final omnidirectional images. When using multi-camera systems, the displacement of the cameras' perspective centres can affect the generation of the omnidirectional images and the resulting accuracy. A common approach to minimising the resulting parallax error is to establish a value for the projection cylinder radius as close as possible to the object's depth. This work proposes a highly accurate simultaneous calibration technique for multiple camera systems using self-calibrating bundle adjustment with constraints of stability of the relative orientation parameters. These parameters are later used to generate a projecting cylindrical surface, maintaining the original camera perspective centres and relative orientation angles. The experiments show that using constraints improved both the calibration results and the final omnidirectional images. Residual mismatches between points in overlapping areas are subpixel.
{"title":"Simultaneous Calibration of Multiple Cameras and Generation of Omnidirectional Images","authors":"José M. Pacheco, A. Tommaselli","doi":"10.5194/isprs-annals-x-1-2024-183-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-2024-183-2024","url":null,"abstract":"Abstract. Omnidirectional images are increasingly being used in various areas, such as urban mapping, virtual reality, agriculture, and robotics. These images can be generated by different acquisition systems, including multi-camera systems, which can acquire higher-resolution images. Stitching techniques are often used and can be suitable for non-metric applications, but rigorous photogrammetric processing is recommended when having more accurate requirements. The main challenges related to this kind of product are the system calibration and the generation of the final omnidirectional images. When using multi-camera systems, the displacement of the cameras' perspective centres can affect the generation of the omnidirectional images and the resulting accuracy. A common approach to minimising the resulting parallax error is to establish a value for the projection cylinder radius as close as possible to the object's depth. This work proposes a highly accurate simultaneous calibration technique for multiple camera systems using self-calibrating bundle adjustment with constraints of stability of the relative orientation parameters. These parameters are later used to generate a projecting cylindrical surface, maintaining the original camera perspective centres and relative orientation angles. The experiments show that using constraints improved both the calibration results and the final omnidirectional images. Residual mismatches between points in overlapping areas are subpixel.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140997202","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 : 2023-12-14DOI: 10.5194/isprs-annals-x-1-w1-2023-1157-2023
W. Huang, B. Y. Chen, F. Biljecki, Y. Yan, Y. Grinberger, H. Li
{"title":"Preface: Workshop “GeoHB 2023: Geo-Spatial Computing for Understanding Human Behaviours”","authors":"W. Huang, B. Y. Chen, F. Biljecki, Y. Yan, Y. Grinberger, H. Li","doi":"10.5194/isprs-annals-x-1-w1-2023-1157-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-w1-2023-1157-2023","url":null,"abstract":"<jats:p> </jats:p>","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"61 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180380","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 : 2023-12-14DOI: 10.5194/isprs-annals-x-1-w1-2023-1159-2023
F. Nex, F. Chiabrando, E. Honkavaara
{"title":"Preface: Workshop “IAMS - Intelligent Autonomous Mapping Systems”","authors":"F. Nex, F. Chiabrando, E. Honkavaara","doi":"10.5194/isprs-annals-x-1-w1-2023-1159-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-w1-2023-1159-2023","url":null,"abstract":"<jats:p> </jats:p>","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"51 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139179560","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 : 2023-12-14DOI: 10.5194/isprs-annals-x-1-w1-2023-1163-2023
J. Boehm, B. Yang, M. Weinmann, K. Anders, R. Wang
{"title":"Preface: Workshop “Laser Scanning 2023”","authors":"J. Boehm, B. Yang, M. Weinmann, K. Anders, R. Wang","doi":"10.5194/isprs-annals-x-1-w1-2023-1163-2023","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-1-w1-2023-1163-2023","url":null,"abstract":"<jats:p> </jats:p>","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"817 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139179130","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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