ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences最新文献

{"title":"Machine Learning Approaches for Vehicle Counting on Bridges Based on Global Ground-Based Radar Data","authors":"Matthias Arnold, Sina Keller","doi":"10.5194/isprs-annals-x-2-2024-1-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-1-2024","url":null,"abstract":"Abstract. This study introduces a novel data-driven approach for classifying and estimating the number of vehicles crossing a bridge solely on non-invasive ground-based radar time series data (GBR data). GBR is used to measure the bridge displacement remotely. It has recently been investigated for remote bridge weigh-in-motion (BWIM). BWIM mainly focuses on single-vehicle events. However, events with several vehicles should be exploited to increase the amount of data. Therefore, extracting the number of involved vehicles in the first step would be beneficial. Acquiring such information from global bridge responses such as displacement can be challenging. This study indicates that a data-driven machine learning approach can extract the vehicle count from GBR time series data. When classifying events according to the number of vehicles, we achieve a balanced accuracy of up to 80 % on an imbalanced dataset. We also try to estimate the number of cars and trucks separately via regression and acquire a R2 of 0.8. Finally, we show the impact of the data augmentation methods we apply to the GBR data to tackle the skew in the dataset using the feature importance of Random Forests.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"110 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361074","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":"Quantitative Evaluation of Color Enhancement Methods for Underwater Photogrammetry in Very Shallow Water: a Case Study","authors":"A. Calantropio, F. Chiabrando, F. Menna, E. Nocerino","doi":"10.5194/isprs-annals-x-2-2024-25-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-25-2024","url":null,"abstract":"Abstract. Underwater photogrammetry is often hampered by chromatic aberration, leading to degraded 2D and 3D products. This study investigates the effectiveness of various color enhancement methods in addressing these challenges.Theoretical considerations indicate that light penetration depth varies inversely with wavelength, causing underwater images to exhibit a blue or green cast with increasing depth. Color enhancement techniques can restore natural colors by compensating for this spectral attenuation. Additionally, scattering, caused by light reflected by particles in the water, can introduce haze into underwater images. Color enhancement can mitigate scatter and improve image clarity. In this contribution, to quantitatively evaluate color enhancement methods, we compare original images with images processed using gray-world assumption methods and physical methods that account for the physical properties of light underwater. Using artificial intelligence (AI) for underwater image color enhancement, a data-driven approach was also employed. These methods were applied to a case study concerning a Roman Navis Lapidaria shipwreck carrying five monumental cipollino marble columns at a depth of 4.5 meters in the Porto Cesareo Marine Protected Area (Italy). These methods were compared quantitatively and qualitatively, and the results are presented and discussed.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364754","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":"Unit-level LoD2 Building Reconstruction from Satellite-derived Digital Surface Model and Orthophoto","authors":"Shengxi Gui, P. Schuegraf, K. Bittner, Rongjun Qin","doi":"10.5194/isprs-annals-x-2-2024-81-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-81-2024","url":null,"abstract":"Abstract. Recent advancements in deep learning have enabled the possibility to identify unit-level building sections from very high resolution satellite images. By learning from the examples, deep models can capture patterns from the low-resolution roof textures to separate building units from duplex buildings. This paper demonstrates that such unit-level segmentation can further advance level of details (LoD)2 modeling. We extend a building boundary regularization method by adapting noisy unit-level segmentation results. Specifically, we propose a novel polygon composition approach to ensure the individually segmented units within a duplex building or dense adjacent buildings are consistent in their shared boundaries. Results of the experiments show that, our unit-level LoD2 modeling has favorably outperformed the state-of-the-art LoD2 modeling results from satellite images.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"108 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361537","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":"Large Scale and Complex Structure Grotto Digitalization Using Photogrammetric Method: A Case Study of Cave No. 13 in Yungang Grottoes","authors":"Hanyu Xiang, Wenyuan Niu, Xianfeng Huang, Bo Ning, Fan Zhang, Jianmin Xu","doi":"10.5194/isprs-annals-x-2-2024-231-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-231-2024","url":null,"abstract":"Abstract. 3D reconstruction of cultural heritage with large volume and high precision is a technical problem in the field of photogrammetry. This paper studies a high-precision digitalization method for large-volume immovable heritage assets based on photogrammetry and laser scanning. It solves the problem of large-scale aerial triangulation and ensures overall color and geometric consistency while satisfying high-precision modeling of local details. Taking the millimeter accuracy 3D reconstruction project of Cave No. 13 in Yungang Grottoes as an example, we use more than 280,000 arbitrary images to reconstruct the entire cave and verify the effectiveness of the proposed method.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141366150","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":"A Novel Approach to Image Retrieval for Vision-Based Positioning Utilizing Graph Topology","authors":"A. Elashry, C. Toth","doi":"10.5194/isprs-annals-x-2-2024-49-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-49-2024","url":null,"abstract":"Abstract. This research introduces a novel approach to improve vision-based positioning in the absence of GNSS signals. Specifically, we address the challenge posed by obstacles that alter image information or features, making retrieving the query image from the database difficult. While the Bag of Visual Words (BoVW) is a widely used image retrieval technique, it has a limitation in representing each image with a single histogram vector or vocabulary of visual words, i.e., the emergence of obstacles can introduce new features to the query image, resulting in different visual words. Our study overcomes this limitation by clustering the features of each image using the k-means method and generating a graph for each class. Each node or key point in the graph obtains additional information from its direct neighbors using functions employed in graph neural networks, functioning as a feedforward network with constant parameters. This process generates new embedding nodes, and eventually, global pooling is applied to produce one vector for each graph, representing each image with graph vectors based on objects or feature classes. As a result, each image is represented with graph vectors based on objects or feature classes. In the presence of obstacles covering one or more graphs, there is sufficient information from the query image to retrieve the most relevant image from the database. Our approach was applied to indoor positioning applications, with the database collected in Bolz Hall at The Ohio State University. Traditional BoVW techniques struggle to properly retrieve most query images from the database due to obstacles like humans or recently deployed objects that alter image features. In contrast, our approach has shown progress in image retrieval by representing each image with multiple graph vectors, depending on the number of objects in the image. This helps prevent or mitigate changes in image features caused by obstacles covering or adding features to the image, as demonstrated in the results.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 1242","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363827","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":"Structural Health Monitoring of Bridges with Personal Laser Scanning: Segment-based Analysis of systematic Point Cloud Deformations","authors":"R. Blaskow, Hans-Gerd Maas","doi":"10.5194/isprs-annals-x-2-2024-9-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-9-2024","url":null,"abstract":"Abstract. Bridge structures can be surveyed using a number of different methods. Established are image-based methods using structure from motion by an unmanned aerial vehicle (UAV), terrestrial laser scanning (TLS), or a combination of both methods. Beyond static terrestrial laser scanning, buildings can also be efficiently surveyed using personal laser scanner (PLS) systems. The advantage here is the greater flexibility and increased speed compared to the static method. On the other hand, the accuracy may be more critical, and the resulting point cloud will be more sensitive to systematic global or local deformations under unfavorable measurement conditions. For example, temporary influences can lead to local mapping errors. These include influences such as uneven measurement system motion or non-static, feature-sparse environments. This study investigates the acquisition of 3D point clouds representing the outer shell of a concrete bridge using a PLS system. We demonstrate a method for detecting possible deformations in PLS point clouds using the example of a bridge structure. For this purpose, the reference (TLS) and the PLS point clouds are segmented into individual clusters and a segment-based ICP fine registration is performed. Different RMSE values for the upper road section (0.061 m) and for the pillar segments (0.021 m) as well as different transformation parameters indicate slight displacements in the PLS point cloud. The analysis of the cloud-to-cloud distances showed that there were slight deformations in the Z direction in the area of the road surface. In the lateral direction, no significant residual deviations were found in the area of the bridge pillars.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364964","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":"A Novel Hyperspectral Salt Assessment Model for Weathering in Architectural Ruins","authors":"Yikang Ren, Fang Liu","doi":"10.5194/isprs-annals-x-2-2024-201-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-201-2024","url":null,"abstract":"Abstract. The Dunhuang murals, a significant part of Chinese heritage, have suffered deterioration primarily due to environmental and chemical factors, notably salt damage. This study proposes a sophisticated method that synergizes Fractional Order Differentiation (FOD) and Partial Least Squares Regression (PLSR) to accurately invert the phosphate content in the Mural Plaster of the Dunhuang paintings. The focal points of the research include: 1) To address the issue of information loss and reduced modeling precision caused by integer order differentiation algorithms, the FOD method is employed for preprocessing hyperspectral data. This approach ensures the fine spectral differences in the phosphate content of the Mural Plaster are precisely captured, 2) Utilizing PLSR, the study models the spectral bands identified at a significance level of 0.01 with measured conductivity values, thereby enabling the precise prediction of the phosphate content in the murals. The research outcomes reveal: 1) The FOD method can elucidate the nonlinear characteristics and variation patterns of the mural samples in the hyperspectral curve.As the order increases from zero to two, the number of spectral bands meeting the 0.01 significance test initially decreases and then increases. The highest absolute value of the positive correlation coefficient is observed at 1.9 orders, corresponding to the 2077 nm band, 2) For predicting the phosphate content in the murals, the model at 1.9 orders is most suitable for inversion. This model, after cross-validation, achieves a maximum R2 value of 0.783. This study created an efficient FOD-based model for estimating phosphate in mural plasters.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364975","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":"Performance analysis of Bayesian optimised gradient-boosted decision trees for digital elevation model (DEM) error correction: interim results","authors":"C. Okolie, A. Adeleke, J. Smit, J. Mills, Caleb O. Ogbeta, I. Maduako","doi":"10.5194/isprs-annals-x-2-2024-179-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-179-2024","url":null,"abstract":"Abstract. Gradient-Boosted Decision Trees (GBDTs), particularly when tuned with Bayesian optimisation, are powerful machine learning techniques known for their effectiveness in handling complex, non-linear data. However, the performance of these models can be significantly influenced by the characteristics of the terrain being analysed. In this study, we assess the performance of three Bayesian-optimised GBDTs (XGBoost, LightGBM and CatBoost) using digital elevation model (DEM) error correction as a case study. The performance of the models is investigated across five landscapes in Cape Town South Africa: urban/industrial, agricultural, mountain, peninsula and grassland/shrubland. The models were trained using a selection of datasets (elevation, terrain parameters and land cover). The comparison entailed an analysis of the model execution times, regression error metrics, and level of improvement in the corrected DEMs. Generally, the optimised models performed considerably well and demonstrated excellent predictive capability. CatBoost emerged with the best results in the level of improvement recorded in the corrected DEMs, while LightGBM was the fastest of all models in the execution time for Bayesian optimisation and model training. These findings offer valuable insights for applying machine learning and hyperparameter tuning in remote sensing.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"123 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361760","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":"Under and Through Water Datasets for Geospatial Studies: the 2023 ISPRS Scientific Initiative “NAUTILUS”","authors":"A. Calantropio, F. Menna, D. Skarlatos, C. Balletti, Gottfried Mandlburger, P. Agrafiotis, F. Chiabrando, A. Lingua, Alessia Giaquinto, E. Nocerino","doi":"10.5194/isprs-annals-x-2-2024-33-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-33-2024","url":null,"abstract":"Abstract. Benchmark datasets have become increasingly widespread in the scientific community as a method of comparison, validation, and improvement of theories and techniques thanks to more affordable means for sharing. While this especially holds for test sites and data collected above the water, publicly accessible benchmark activities for geospatial analyses in the underwater environment are not very common. Applying geomatic techniques underwater is challenging and expensive, especially when dealing with deep water and offshore operations. Moreover, benchmarking requires ground truth data for which, in water, several open issues exist concerning geometry and radiometry. Recognizing this scientific and technological challenge, the NAUTILUS (uNder And throUgh waTer datasets for geospatIaL stUdieS) project aims to create guidelines for new multi-sensor/cross-modality benchmark datasets. The project focuses on (i) surveying the actual needs and gaps in through and under-the-water geospatial applications through a questionnaire and interviews, (ii) launching a unique publicly available database collecting already existing datasets scattered across the web and literature, (iii) designing and identifying proper test site(s) and methodologies to deliver to the extended underwater community a brand-new multi-sensor/cross-modality benchmark dataset. The project outputs are available to researchers and practitioners in underwater measurements-related domains, as they can now access a comprehensive tool providing a synthesis of open questions and data already available. In doing so, past research efforts to collect and publish datasets have received additional credit and visibility.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 498","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364353","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":"The Potential of Neural Radiance Fields and 3D Gaussian Splatting for 3D Reconstruction from Aerial Imagery","authors":"D. Haitz, Max Hermann, Aglaja Solana Roth, Michael Weinmann, Martin Weinmann","doi":"10.5194/isprs-annals-x-2-2024-97-2024","DOIUrl":"https://doi.org/10.5194/isprs-annals-x-2-2024-97-2024","url":null,"abstract":"Abstract. In this paper, we focus on investigating the potential of advanced Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting for 3D scene reconstruction from aerial imagery obtained via sensor platforms with an almost nadir-looking camera. Such a setting for image acquisition is convenient for capturing large-scale urban scenes, yet it poses particular challenges arising from imagery with large overlap, very short baselines, similar viewing direction and almost the same but large distance to the scene, and it therefore differs from the usual object-centric scene capture. We apply a traditional approach for image-based 3D reconstruction (COLMAP), a modern NeRF-based approach (Nerfacto) and a representative for the recently introduced 3D Gaussian Splatting approaches (Splatfacto), where the latter two are provided in the Nerfstudio framework. We analyze results achieved on the recently released UseGeo dataset both quantitatively and qualitatively. The achieved results reveal that the traditional COLMAP approach still outperforms Nerfacto and Splatfacto approaches for various scene characteristics, such as less-textured areas, areas with high vegetation, shadowed areas and areas observed from only very few views.\u0000","PeriodicalId":508124,"journal":{"name":"ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141365837","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}