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CONSTRAINED BUNDLE ADJUSTMENT FOR STRUCTURE FROM MOTION USING UNCALIBRATED MULTI-CAMERA SYSTEMS

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arXiv 2022年

作者： Huang, Debao Elhashash, Mostafa Qin, Rongjun Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Structure from motion using uncalibrated multi-camera systems is a challenging task. This paper proposes a bundle adjustment solution that implements a baseline constraint respecting that these cameras are static to each other. We assume these cameras are mounted on a mobile platform, uncalibrated, and coarsely synchronized. To this end, we propose the baseline constraint that is formulated for the scenario in which the cameras have overlapping views. The constraint is incorporated in the bundle adjustment solution to keep the relative motion of different cameras static. Experiments were conducted using video frames of two collocated GoPro cameras mounted on a vehicle with no system calibration. These two cameras were placed capturing overlapping contents. We performed our bundle adjustment using the proposed constraint and then produced 3D dense point clouds. Evaluations were performed by comparing these dense point clouds against LiDAR reference data. We showed that, as compared to traditional bundle adjustment, our proposed method achieved an improvement of 29.38%. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

Image Fusion in Remote Sensing: An Overview and Meta-Analysis

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SSRN

SSRN 2024年

作者： Albanwan, Hessah Qin, Rongjun Tang, Yang Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. It greatly facilitates downstream applications such as pan-sharpening, change detection, land-cover classification, etc. Yet, image fusion solutions are highly disparate to various remote sensing problems and thus are often narrowly defined in existing reviews as topical applications, such as pan-sharpening and spatial-temporal image fusion. Considering that image fusion can be theoretically applied to any gridded data through pixel-level operations, in this paper, we expand its scope by comprehensively surveying relevant works with a simple taxonomy: 1) many-to-one image fusion;2) many-to-many image fusion. This simple taxonomy defines image fusion as a mapping problem that turns either a single or a set of images into another single or set of images, depending on the desired coherence, e.g., spectral, spatial/resolution coherence, etc. We show that this simple taxonomy, despite the significant modality difference it covers, can be presented by a conceptually easy framework. Furthermore, we provide a meta-analysis to review the major papers studying the various types of image fusion and their applications over the years (from the 1980s to date), covering 10,420 peer-reviewed papers. Finally, we discuss image fusion's main benefits and emerging challenges to provide open research directions and potential future works. © 2024, The Authors. All rights reserved.

关键词： Image fusion

Using PlanetScope-derived time-series elevation models and orthophotos to track glacier 3D dynamics in mid-latitude mountain regions

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Mapping Sciences and Remote Sensing 2025年第1期62卷

作者： Shengxi Gui Rongjun Qin a Geospatial Data Analytics Lab The Ohio State University Columbus OH USAb Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus OH USA a Geospatial Data Analytics Lab The Ohio State University Columbus OH USAb Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus OH USAc Department of Electrical and Computer Engineering The Ohio State University Columbus OH USAd Translational Data Analytics Institute The Ohio State University Columbus OH USA

ABSTRACTAssessing glacier surface height changes provides crucial insights into glacier mass loss and the impact of climate variability. Remote sensing images play a vital role in providing data points for monitoring. Oftentimes, the glacier height change observation is sparse with notable time intervals, yet it is unclear whether these sparse observations are within seasonal variations. Thus, data with high-temporal resolution are necessary to 1) establish sufficiently dense observations to achieve enhanced conclusions; 2) derive higher-level data, such as motion velocity, to assess the level of global warming and climate change. PlanetScope satellite constellations can provide global daily/weekly observations with a 4 m ground sample distance (GSD). In this work, we report a study on monitoring fast dynamic glaciers in mid-latitude mountain regions in America and Asia using the derived time-series 3D elevation models from PlanetScope. The study includes three sites: La Perouse Glacier (North America), Viedma Glacier (South America), and Skamri Glacier (Central Asia). Based on PlanetScope data, we derived near bi-monthly 4-m resolution 3D elevation models for the year of 2019–2023 using satellite stereo photogrammetry, to track the ice flow in 3D. The results can be used to decorrelate the factors from seasonable variation: the Viedma Glacier is observed thinner over time with a slower flow rate; although through a single and short-term observation, the thinning rate of La Perouse Glacier is shown accelerating, we did not observe evidence that La Perouse Glacier and Skamri Glacier have obvious thinning other than its seasonable variations within a few years of time frames (since the PlanetScope images were available). By focusing on the local surface motion variations, our study shows that the glacier velocity varies with regional climate, geography, and hydrology, with a notable 45-day lag response between climate factors and flow speed in Viedma and Skamri Glacie

关键词： Glacier dynamics 3D reconstruction satellite stereo matching glacier surface motion PlanetScope

Automated LoD-2 model reconstruction from very-high-resolution satellite-derived digital surface model and orthophoto

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arXiv 2021年

作者： Gui, Shengxi Qin, Rongjun Geospatial Data Analytics Lab Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering Ohio State University Columbus United States Department of Electrical and Computer Engineering Ohio State University Columbus United States Translational Data Analytics Institute Ohio State University Columbus United States

Digital surface models (DSM) generated from multi-stereo satellite images are getting higher in quality owing to the improved data resolution and photogrammetric reconstruction algorithms. Very-high-resolution (VHR, with sub-meter level resolution) satellite images effectively act as a unique data source for 3D building modeling, because it provides a much wider data coverage with lower cost than the traditionally used LiDAR and airborne photogrammetry data. Although 3D building modeling from point clouds has been intensively investigated, most of the methods are still ad-hoc to specific types of buildings and require high-quality and high-resolution data sources as input. Therefore, when applied to satellite-based point cloud or DSMs, these developed approaches are not readily applicable and more adaptive and robust methods are needed. As a result, most of the existing work on building modeling from satellite DSM achieves LoD-1 generation. In this paper, we propose a model-driven method that reconstructs LoD-2 building models following a "decomposition-optimization-fitting" paradigm. The proposed method starts building detection results through a deep learning-based detector and vectorizes individual segments into polygons using a "three-step" polygon extraction method, followed by a novel grid-based decomposition method that decomposes the complex and irregularly shaped building polygons to tightly combined elementary building rectangles ready to fit elementary building models. We have optionally introduced OpenStreetMap (OSM) and Graph-Cut (GC) labeling to further refine the orientation of 2D building rectangle. The 3D modeling step takes building-specific parameters such as hip lines, as well as non-rigid and regularized transformations to optimize the flexibility for using a minimal set of elementary models. Finally, roof type of building models s refined and adjacent building models in one building segment are merged into the complex polygonal model. Our propo

关键词： Geometry

Large-scale and Efficient Texture Mapping Algorithm via Loopy Belief Propagation

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arXiv 2023年

作者： Ling, Xiao Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States College of Astronautics Nanjing University Of Aeronautics And Astronautics 29 Jiangjun Avenue Jiangning District Jiangsu Nanjing211106 China Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Texture mapping as a fundamental task in 3D modeling has been well established for well-acquired aerial assets under consistent illumination, yet it remains a challenge when it is scaled to large datasets with images under varying views and illuminations. A well-performed texture mapping algorithm must be able to efficiently select views, fuse and map textures from these views to mesh models, at the same time, achieve consistent radiometry over the entire model. Existing approaches achieve efficiency either by limiting the number of images to one view per face, or simplifying global inferences to only achieve local color consistency. In this paper, we break this tie by proposing a novel and efficient texture mapping framework that allows the use of multiple views of texture per face, at the same time to achieve global color consistency. The proposed method leverages a loopy belief propagation algorithm to perform an efficient and global-level probabilistic inferences to rank candidate views per face, which enables face-level multi-view texture fusion and blending. The texture fusion algorithm, being non-parametric, brings another advantage over typical parametric post color correction methods, due to its improved robustness to non-linear illumination differences. The experiments on three different types of datasets (i.e. satellite dataset, unmanned-aerial vehicle dataset and close-range dataset) show that the proposed method has produced visually pleasant and texturally consistent results in all scenarios, with an added advantage of consuming less running time as compared to the state of the art methods, especially for large-scale dataset such as satellite-derived models. Copyright © 2023, The Authors. All rights reserved.

关键词： Belief propagation

Spatio-Temporal Stratified Associations between Urban Human Activities and Crime Patterns: A Case Study in San Francisco Around the Covid-19 Stay-at-Home Mandate

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SSRN

SSRN 2022年

作者： Chen, Tongxin Bowers, Kate Zhu, Di Gao, Xiaowei Cheng, Tao Space Time Lab for Big Data Analytics Department of Civil Environmental and Geomatic Engineering University College London LondonWC1E 6BT United Kingdom Department of Security and Crime Science University College London LondonWC1H 9EZ United Kingdom Geospatial Data Intelligence Lab Department of Geography Environment and Society University of Minnesota Twin Cities Minneapolis55455 United States

Crime changes have been reported as a result of human routine activity shifting due to containment policies, such as stay-at-home (SAH) mandates during the COVID-19 pandemic. However, the way in which the manifestation of crime in both space and time is affected by dynamic human activities in urban areas has not been explored in depth in empirical studies hitherto. Here, we aim to quantitatively measure the spatio-temporal associations between crime patterns and human activities in the context of an unstable period of the ever-changing socio-demographic backcloth. We propose an analytical framework to detect the dynamic stratified associations between the spatial distributions of human activities and crimes. The results of a case study in San Francisco, United States reveal that the spatial patterns of most crime types are statistically significantly associated with that of human activities zones. Property crime exhibits higher stratified association than violent crime across all temporal scales. Further, the strongest association is obtained with the eight-week time span centered around the SAH order. These findings not only enhance our understanding of the relationships between urban crime and human activities, but also offer insights that tailored crime intervention strategies need to consider human activity variables. © 2022, The Authors. All rights reserved.

关键词： Crime

A Graph-Matching Approach for Cross-view Registration of Over-view 2 and Street-view based Point Clouds

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arXiv 2022年

作者： Ling, Xiao Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Wide-area 3D data generation for complex urban environments often needs to leverage a mixed use of data collected from both air and ground platforms, such as from aerial surveys, satellite, and mobile vehicles, etc. On one hand, such kind of data with information from drastically different views (ca. 90°and more) forming cross-view data, which due to very limited overlapping region caused by the drastically different line of sight of the sensors, is difficult to be registered without significant manual efforts. On the other hand, the registration of such data often suffers from non-rigid distortion of the street-view data (e.g., non-rigid trajectory drift), which cannot be simply rectified by a similarity transformation. In this paper, based on the assumption that the object boundaries (e.g., buildings) from the over-view data should coincide with footprints of façade 3D points generated from street-view photogrammetric images, we aim to address this problem by proposing a fully automated geo-registration method for cross-view data, which utilizes semantically segmented object boundaries as view-invariant features under a global optimization framework through graph-matching: taking the over-view point clouds generated from stereo/multi-stereo satellite images and the street-view point clouds generated from monocular video images as the inputs, the proposed method models segments of buildings as nodes of graphs, both detected from the satellite-based and street-view based point clouds, thus to form the registration as a graph-matching problem to allow non-rigid matches;to enable a robust solution and fully utilize the topological relations between these segments, we propose to address the graph-matching problem on its conjugate graph solved through a belief-propagation algorithm. The matched nodes will be subject to a further optimization to allow precise-registration, followed by a constrained bundle adjustment on the street-view image to keep 2D-3D consistencies, w

关键词： Global optimization

A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images

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arXiv 2022年

作者： Albanwan, Hessah Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robust DL methods are on satellite stereo images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereo matching methods through hundreds of multi-date multi-site satellite stereo pairs with varying geometric configurations, against the traditional well-practiced Census-SGM (Semi-global matching), to comprehensively understand their accuracy, robustness, generalization capabilities, and their practical potential. The DL methods include a learning-based cost metric through convolutional neural networks (MC-CNN) followed by SGM, and three end-to-end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalize well for unseen data. The learning-based cost metric and Census-SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereo pairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalize on satellite images when trained on different datasets (airborne or ground-view). Copyright © 2022, The Authors. All rights reserved.

关键词： Satellites

A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse

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arXiv 2021年

作者： Xu, Ningli Huang, Debao Song, Shuang Ling, Xiao Strasbaugh, Chris Yilmaz, Alper Sezen, Halil Qin, Rongjun Geospatial Data Analytics Lab The Ohio State University Columbus United States Photogrammetric Computer Vision Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States Engineering Technology Services The Ohio State University Columbus United States

In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event. Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition. The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds, achieving an average distance of 12 cm and 16 cm for roof and façade respectively. We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation (free of ground control points), reconstruction, and a coarse-to-fine 3D density change analysis. This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively. Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92, with consistently high precision (0.92 - 0.99). Volumetric changes through the demolition progress are derived from change detection and have shown to favorably reflect the qualitative and quantitative building demolition progression. Copyright © 2021, The Authors. All rights reserved.

关键词： Photogrammetry