The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences最新文献

{"title":"CHALLENGES IN PREPARING DATASETS FOR SUPER-RESOLUTION ON THE EXAMPLE OF SENTINEL-2 AND PLANET SCOPE IMAGES","authors":"A. Malczewska, J. Malczewski, B. Hejmanowska","doi":"10.5194/isprs-archives-xlviii-1-w3-2023-91-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-91-2023","url":null,"abstract":"Abstract. Benchmark datasets is an significant aspect in in many areas such as computer vision, deep learning, geospatial data as they serve as standardized test sets for evaluating the performance of models. Among many techniques of image processing, there is super-resolution (SR) which is aimed at reconstructing a low-resolution (LR) image into a high-resolution (HR) image. For training and validation SR models as a dataset the pairs of HR and LR images are needed, which should be the same apart from resolution. There is a lot of benchmark datasets for super-resolution methods, but they usually include conventional photographs of an common objects, while remote sensing data have different characteristic in general. This paper focuses on the process of preparing datasets for super-resolution in satellite images, where high-resolution and low-resolution image data come from different sources. The case of the single-image super-resolution method was considered. The experiment was performed on Sentinel-2 and PlanetScope data, but the assumptions can also be transferred to data obtained from other satellites. The procedure on how to make the pairs of HR and LR images consistent in terms of time, location and spectral values was proposed. The impact of the processes carried out was measured using image similarity measurement methods such as PSNR, SSIM and SCC.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778435","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":"EVALUATION OF A GNSS/IMU/LIDAR-INTEGRATION FOR AIRBORNE LASER SCANNING USING RTKLIB PPK AND PPP GNSS SOLUTIONS","authors":"F. Pöppl, G. Mandlburger, N. Pfeifer","doi":"10.5194/isprs-archives-xlviii-1-w3-2023-161-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-161-2023","url":null,"abstract":"Abstract. Airborne laser scanning allows for efficient acquisition of accurate 3D data for large areas. Because georeferencing of the LiDAR data requires knowledge of the platform trajectory, the laser scanner system commonly comprises a global navigation satellite system (GNSS) receiver/antenna and an inertial measurement unit (IMU). The standard processing pipeline consists of GNSS/IMU integration, georeferencing, and subsequent adjustment of the laser data. Here, we consider a holistic GNSS/IMU/LiDAR-integration approach based on least-squares adjustment. The GNSS is loosely coupled, and the GNSS positions are obtained using either postprocessing kinematic or precise point positioning GNSS processing strategies using the open-source software RTKLib. In this contribution, we compare the resulting point clouds to those of a standard processing workflow and evaluate the impact of the different processing strategies on point cloud quality in terms of internal consistency and absolute accuracy for a airborne laser bathymetry (ALB) dataset. Although the GNSS solutions themselves differ strongly, both the PPK- and the PPP-derived point clouds show better strip differences (below 2.5 cm) and similar absolute accuracy (<4 cm RMSE w.r.t. reference targets after correction of constant datum shift) compared to the reference solution.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135728982","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":"NERFBK: A HOLISTIC DATASET FOR BENCHMARKING NERF-BASED 3D RECONSTRUCTION","authors":"Z. Yan, G. Mazzacca, S. Rigon, E. M. Farella, P. Trybala, F. Remondino","doi":"10.5194/isprs-archives-xlviii-1-w3-2023-219-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-219-2023","url":null,"abstract":"Abstract. Neural Radiance Field methods are innovative solutions to derive 3D data from a set of oriented images. This paper introduces new real and synthetic image datasets - called NeRFBK - specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. More and more reconstruction algorithms and techniques are available nowadays, raising the need to evaluate and compare the quality of derived 3D products currently used in various domains and applications. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK set of data, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729593","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":"CURRENT STATUS OF THE BENCHMARK DATABASE BEMEDA","authors":"L. E. Budde, J. Schmidt, T. Kullmann, D. Iwaszczuk","doi":"10.5194/isprs-archives-xlviii-1-w3-2023-25-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-25-2023","url":null,"abstract":"Abstract. Open science is an important attribute for developing new approaches. Especially, the data component plays a significant role. The FAIR principle provides a good orientation towards open data. One part of FAIR is findability. Thus, domain specific dataset search platforms were developed: the Earth Observation Database and our Benchmark Metadata Database (BeMeDa). In addition to the search itself, the datasets found by this platforms can be compared with each other with regard to their interoperability. We compare these two platforms and present an update of our platform BeMeDa. This update includes additional location information about the datasets and a new frontend design with improved usability. We rely on user feedback for further improvements and enhancements.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135730462","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":"COMPARISON OF IPHONE 13 PRO'S CAMERA AND LIDAR SENSOR TO UAS PHOTOGRAMMETRIC MODEL OF THE GREAT PYRAMID OF GIZA","authors":"R. Tamimi, C. Toth","doi":"10.5194/isprs-archives-xlviii-m-3-2023-299-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-299-2023","url":null,"abstract":"Abstract. Digital documentation of historical sites has always required the use of expensive professional grade sensors capable of collecting large amounts of data to reconstruct cultural sites. These types of projects generally require large budgets and a large team of specialists to successfully generate a digital model. However, with smart devices having sensors capable of mapping on the go, the potential for mapping such historical sites may be more accessible. This study aims to conduct a comprehensive comparison between the iPhone 13 Pro and the Unmanned Aerial Systems (UAS) photogrammetric model of the Great Pyramid of Giza, otherwise known as the Khufu pyramid, located in Giza, Egypt. The purpose of this study is to evaluate the potential of the iPhone 13 Pro's Camera and LiDAR sensor capabilities as a valuable tool for documenting and preserving cultural heritage sites. To accomplish this, data was captured from multiple positions around the pyramid using the Pix4Dcatch app on the iPhone 13 Pro, and the data was processed using Pix4Dmatic to generate a 3D point cloud of the pyramid. This point cloud data is then compared to the reference data obtained through the UAS mapping which generated a 3D photogrammetric model. The comparison aims to identify the strengths and weaknesses of using the iPhone 13 Pro for this type of scanning and to assess the accuracy and precision of the generated data.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135992923","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 ASSESSMENT OF A MINI MOBILE MAPPING SYSTEM: IPHONE 14 PRO INSTALLED ON A E-SCOOTER","authors":"R. Tamimi, C. Toth","doi":"10.5194/isprs-archives-xlviii-m-3-2023-307-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-307-2023","url":null,"abstract":"Abstract. In this study, we investigate the feasibility of using an iPhone 14 Pro's camera and LiDAR sensors to collect high-accuracy spatial data on a mobile e-scooter. Given the widespread availability of e-scooters in urban areas, they present an ideal platform for creating a compact mobile mapping system. The iPhone is securely mounted on the e-scooter and paired with a viDoc RTK Rover, which offers real-time kinematic (RTK) positioning accuracy in open sky areas. As the e-scooter traverses the area of interest, data is collected using the LiDAR sensor, while images are captured using the camera. The collected data is then processed using Pix4Dmatic software, enabling the generation of a fused point cloud and a detailed digital model of the surveyed area. In situations where the Global Navigation Satellite System (GNSS) signal is compromised or unavailable, such as indoor environments or urban canyons, alternative methods like Simultaneous Localization and Mapping (SLAM) can be employed. Additionally, Total Stations can be utilized to track the entire system's movement in GNSS-denied environments and provide accurate georeferencing for the acquired data. Control and check points throughout the area of interest are established using the Total Station as well. This approach offers a flexible and cost-effective means of collecting high-accuracy spatial data in small areas across a variety of environments, leveraging the readily available e-scooters for public use. The results of various experiments conducted using an iPhone 14 Pro and viDoc RTK on an e-scooter are thoroughly analyzed and reported in this paper.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135995191","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":"BUILT-UP AREA DYNAMICS IN “PRE- AND DURING BOKO HARAM CONFLICTS” IN MONGUNO LOCAL GOVERNMENT AREA OF BORNO STATE, NIGERIA","authors":"A. Bala, T. Youngu, S. Azua, A. O. Aliyu, S. Yabo, A. U. Aliyu","doi":"10.5194/isprs-archives-xlviii-m-3-2023-293-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-293-2023","url":null,"abstract":"Abstract. The Boko Haram insurgency has had a significant impact on the Monguno Local Government Area (LGA) in Borno State, Nigeria, for more than ten years. This study analysed how the Boko Haram insurgency affected the built-up areas of Monguno LGA. The focus of this study was to map, analyse, and detect the spectral and spatial changes of the earth's surface using remotely sensed images and geospatial techniques, focusing in particular on the built-up areas in the study area, in order to provide sufficient information on the status of built-up areas for effective planning and good governance. Employing a combination of the pixel-based Supervised Maximum Likelihood classification algorithm and the Object Based Image analysis, the study used Landsat 7 ETM+ satellite imageries for the years 2004 and 2007 and Landsat 8 OLT/TIRS imageries for the years 2014 and 2021 to determine the rate of change in the built-up areas over a period of seventeen (17) years (from 2004 to 2021). The classified Land Use and Land Cover (LULC) maps were grouped into four classes: water body, built-up areas, vegetation, and bare land, even though the study was more concerned about the changes in the built-up area. The results showed that from 2004, the built-up area occupied 0.12% with a total land area of 2.00km2 and increased in 2007 by 0.21%. From 2007 to 2014 the built-up area was seen to have increased by 0.31% with a built-up area of 6.00km2. Similarly, there was an increase in the built-up area from 0.31% in 2014 to 0.63% in 2021. Generally, the built-up area has increased by 0.51% from 2004–2021 and the largest percentage increase was noticed from 2014 to 2021 where there was an increase of 0.31% in the built-up area. This increase signifies that there has been an inflow of people into Monguno from neighbouring LGA. It was recommended that future research should incorporate other parameters such as population, literacy level, and socioeconomic well-being of the people.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42138951","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":"FOREST FIRE BURNT AREA EXTRACTION USING FUZZY INTEGRATION OF MULTI-SENSOR SATELLITE DATA FOR THE HIMALAYAN STATE","authors":"S. Mamgain, H. C. Karnatak, A. Roy","doi":"10.5194/isprs-archives-xlviii-m-3-2023-285-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-285-2023","url":null,"abstract":"Abstract. Burnt area assessment due to forest fires is an important aspect to estimate the extent of loss of biodiversity which has become feasible even in hilly and inaccessible areas with the help of geospatial technologies. But satellite data also has some limitations as it increases commission error by misclassifying non-burnt areas as burnt areas. To reduce this commission error, present study has attempted to integrate multi-sensor satellite data to characterize and extract forest fire burnt areas in Uttarakhand which is a fire prone hilly state in Western Himalaya. Landsat-8 and Sentinel-2 optical datasets have been used to calculate eleven vegetation/burn indices to identify burn patches for fire season of 2022 (February to June). These vegetation/burn indices have been calculated from Landsat-8 and Sentinel-2 datasets and integrated using Fuzzy Logic Modelling to get characterized forest fire burnt area maps. Accuracy assessment has been done using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points for the characterized map of burnt area by Landsat-8, Sentinel-2 and combining indices from both sensors. The fuzzy map of burnt area using Landsat-8 showed the accuracy of 66.25%, while Sentinel-2 showed accuracy of 59.79% and the integration of fuzzy burnt area maps of both sensors showed the highest accuracy of 79.66%. This information of characterized burnt areas of a region can help forest managers to identify high vulnerable areas to focus on during the fire season to prevent the losses to natural resources, life and property in the region.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47617996","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":"IMPROVING CAMERA POSE ESTIMATION USING SWARM PARTICLE ALGORITHMS","authors":"A. Elashry, C. Toth","doi":"10.5194/isprs-archives-xlviii-m-3-2023-87-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-87-2023","url":null,"abstract":"Abstract. Most computer vision and photogrammetry applications rely on accurately estimating the camera pose, such as visual navigation, motion tracking, stereo photogrammetry, and structure from motion. The Essential matrix is a well-known model in computer vision that provides information about the relative orientation between two images, including the rotation and translation, for calibrated cameras with a known camera matrix. To estimate the Essential matrix, the camera calibration matrices, which include focal length and principal point location must be known, and the estimation process typically requires at least five matching points and the use of robust algorithms, such as RANSAC to fit a model to the data as a robust estimator. From the usually large number of matched points, choosing five points, the Essential matrix can be determined based on a simple solution, which could be good or bad. Obtaining a globally optimal and accurate camera pose estimation, however, requires additional steps, such as using evolutionary algorithms (EA) or swarm algorithms (SA), to prevent getting trapped in local optima by searching for solutions within a potentially huge solution space.This paper aims to introduce an improved method for estimating the Essential matrix using swarm particle algorithms that are known to efficiently solve complex problems. Various optimization techniques, including EAs and SAs, such as Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA), are explored to obtain the global minimum of the reprojection error for the five-point Essential matrix estimation based on using symmetric geometric error cost function. The experimental results on a dataset with known camera orientation demonstrate that the IGWO method has achieved the best score compared to other techniques and significantly speeds up the camera pose estimation for larger number of point pairs in contrast to traditional methods that use the collinearity equations in an iterative adjustment.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48414765","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":"BUILDING EDGE DETECTION FROM VERY HIGH-RESOLUTION REMOTE SENSING IMAGERY USING DEEP LEARNING","authors":"D. Prabhakar, P. K. Garg","doi":"10.5194/isprs-archives-xlviii-m-3-2023-189-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-189-2023","url":null,"abstract":"Abstract. Detection of Building edges is crucial for building information extraction and description. Extracting structures from large-scale aerial images has been utilized for years in cartography. With commercially available high-resolution satellites, many aerial photography usages can now employ satellite imagery. Edge detection is focused on pinpointing distinct transitions between greyscale image regions and attributing their origins to underlying physical processes. Detecting building boundaries from very high-resolution (VHR) remote sensing data is essential for many geo-related applications, such as urban planning and management, surveying and mapping, 3D reconstruction, motion recognition, image registration, image enhancement and restoration, image compression, and more. The rapid evolution of convolutional neural networks (CNNs) has led to substantial breakthroughs in edge detection in recent years. Sharp, localized changes in brightness characterize edges in digital images. In most cases, edge detection requires some kind of image smoothing and separation. Differentiation is an ill-conditioned problem, and smoothing leads to information loss. It is challenging to create an edge detection method that works everywhere and adapts to any future processing stages. Therefore, throughout the development of digital image processing, numerous edge detectors have been created, each with its own unique set of mathematical and algorithmic properties. Several edge detectors have been developed due to application needs and the subjective nature of edge definition and characterization. We propose a deep learning technique, particularly convolutional neural networks(CNNs), that offers a promising approach to automatically learn and extract features from very high-resolution remote sensing imagery, leading to more accurate and efficient building edge detection.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42367327","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}