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Remote Sensing
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3D Reconstruction Based on Aerial and Satellite Imagery
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3D Reconstruction Based on Aerial and Satellite Imagery

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 32724

Special Issue Editors

Dr. Friedrich Fraundorfer

E-Mail Website
Guest Editor
Graz University of Technology, Institute of Computer Graphics & Vision Inffeldgasse 16/II, 8010 Graz, Austria
Interests: computer vision; unmanned aerial vehicles; robotics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Pablo D’Angelo

E-Mail Website
Guest Editor
German Aerospace Center (DLR), Remote Sensing Technology Institute, Photogrammetry and Image Analysis, Oberpfaffenhofen, 82234 Weßling, Germany
Interests: satellite image analysis; 3D computer vision, image orientation, semantic modeling

Special Issue Information

Dear Colleagues,

3D reconstructions based on Aerial and Satellite Imagery are the basis for invaluable map services available to anybody. 3D map services are now used by any individual and not only by experts. Keeping all these maps up-to-date is a perpetual process, and this process will benefit from innovations in 3D reconstruction techniques.

The recent advent of deep learning also brought new possibilities of improvements to the field of 3D reconstruction from aerial and satellite imagery. Deep learning methods can be used to improve the underlying fundamental techniques of stereo image matching. Multi-view stereo methods developed for close-range photogrammetry will also be applicable to aerial and satellite imagery. In addition, deep learning methods also allow the fostering of new techniques for reconstructing environments like urban areas and buildings from 3D data created by image matching. This might allow for dynamic 3D reconstructions or even functional 3D reconstructions on the scale of whole urban areas.

In this context, this Special Issue aims to cover recent progress in areas related to 3D reconstruction based on aerial and satellite imagery. The topics should include:

  • Methods for stereo image matching and multi-view image matching of aerial and satellite imagery;
  • 3D reconstruction of buildings or urban areas from 3D data;
  • Building modelling;
  • Map updating and change detection;
  • Dynamic, semantic, and functional 3D reconstructions or maps; and
  • Fusion of aerial imagery, satellite imagery, UAV imagery, terrestrial imagery.

Dr. Friedrich Fraundorfer
Dr. Pablo d’Angelo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • stereo matching
  • multi-view stereo
  • building reconstruction
  • 3D reconstruction
  • image fusion
  • change detection

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Published Papers (5 papers)

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Research

52 pages, 13183 KiB  
Article
Mapping with Pléiades—End-to-End Workflow
by Roland Perko, Hannes Raggam and Peter M. Roth
Remote Sens. 2019, 11(17), 2052; https://doi.org/10.3390/rs11172052 - 1 Sep 2019
Cited by 22 | Viewed by 8354
Abstract
In this work, we introduce an end-to-end workflow for very high-resolution satellite-based mapping, building the basis for important 3D mapping products: (1) digital surface model, (2) digital terrain model, (3) normalized digital surface model and (4) ortho-rectified image mosaic. In particular, we describe [...] Read more.
In this work, we introduce an end-to-end workflow for very high-resolution satellite-based mapping, building the basis for important 3D mapping products: (1) digital surface model, (2) digital terrain model, (3) normalized digital surface model and (4) ortho-rectified image mosaic. In particular, we describe all underlying principles for satellite-based 3D mapping and propose methods that extract these products from multi-view stereo satellite imagery. Our workflow is demonstrated for the Pléiades satellite constellation, however, the applied building blocks are more general and thus also applicable for different setups. Besides introducing the overall end-to-end workflow, we need also to tackle single building blocks: optimization of sensor models represented by rational polynomials, epipolar rectification, image matching, spatial point intersection, data fusion, digital terrain model derivation, ortho rectification and ortho mosaicing. For each of these steps, extensions to the state-of-the-art are proposed and discussed in detail. In addition, a novel approach for terrain model generation is introduced. The second aim of the study is a detailed assessment of the resulting output products. Thus, a variety of data sets showing different acquisition scenarios are gathered, allover comprising 24 Pléiades images. First, the accuracies of the 2D and 3D geo-location are analyzed. Second, surface and terrain models are evaluated, including a critical look on the underlying error metrics and discussing the differences of single stereo, tri-stereo and multi-view data sets. Overall, 3D accuracies in the range of 0.2 to 0.3 m in planimetry and 0.2 to 0.4 m in height are achieved w.r.t. ground control points. Retrieved surface models show normalized median absolute deviations around 0.9 m in comparison to reference LiDAR data. Multi-view stereo outperforms single stereo in terms of accuracy and completeness of the resulting surface models. Full article
(This article belongs to the Special Issue 3D Reconstruction Based on Aerial and Satellite Imagery)
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37 pages, 34428 KiB  
Article
Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery
by Tahmineh Partovi, Friedrich Fraundorfer, Reza Bahmanyar, Hai Huang and Peter Reinartz
Remote Sens. 2019, 11(14), 1660; https://doi.org/10.3390/rs11141660 - 11 Jul 2019
Cited by 30 | Viewed by 7762
Abstract
Recent advances in the availability of very high-resolution (VHR) satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for automatic 3-D building model reconstruction which require large-scale and frequent updates, such as [...] Read more.
Recent advances in the availability of very high-resolution (VHR) satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for automatic 3-D building model reconstruction which require large-scale and frequent updates, such as disaster monitoring and urban management. Digital Surface Models (DSMs) generated from stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting in rough building shape representations. To handle 3-D building model reconstruction using such low-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs together with orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satellite imagery. The algorithm consists of multiple steps including building boundary extraction and decomposition, image-based roof type classification, and initial roof parameter computation which are prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM (nDSM) and to select the best one, a parameter optimization method based on exhaustive search is used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building block are intersected to reconstruct the 3-D model of connecting roofs. All corresponding experiments are conducted on a dataset including four different areas of Munich city containing 208 buildings with different degrees of complexity. The results are evaluated both qualitatively and quantitatively. According to the results, the proposed approach can reliably reconstruct 3-D building models, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provides a high level of automation by limiting the number of primitive roof types and by performing automatic parameter initialization. Full article
(This article belongs to the Special Issue 3D Reconstruction Based on Aerial and Satellite Imagery)
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Graphical abstract

22 pages, 7790 KiB  
Article
Multi-Task cGAN for Simultaneous Spaceborne DSM Refinement and Roof-Type Classification
by Ksenia Bittner, Marco Körner, Friedrich Fraundorfer and Peter Reinartz
Remote Sens. 2019, 11(11), 1262; https://doi.org/10.3390/rs11111262 - 28 May 2019
Cited by 20 | Viewed by 5936
Abstract
Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models compared to separately trained models. In [...] Read more.
Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models compared to separately trained models. In this paper, we make an observation of such influences for important remote sensing applications like elevation model generation and semantic segmentation tasks from the stereo half-meter resolution satellite digital surface models (DSMs). Mainly, we aim to generate good-quality DSMs with complete, as well as accurate level of detail (LoD)2-like building forms and to assign an object class label to each pixel in the DSMs. For the label assignment task, we select the roof type classification problem to distinguish between flat, non-flat, and background pixels. To realize those tasks, we train a conditional generative adversarial network (cGAN) with an objective function based on least squares residuals and an auxiliary term based on normal vectors for further roof surface refinement. Besides, we investigate recently published deep learning architectures for both tasks and develop the final end-to-end network, which combines different models, as using them first separately, they provide the best results for their individual tasks. Full article
(This article belongs to the Special Issue 3D Reconstruction Based on Aerial and Satellite Imagery)
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18 pages, 6039 KiB  
Article
Evaluation of Matching Costs for High-Quality Sea-Ice Surface Reconstruction from Aerial Images
by Jae-In Kim, Chang-Uk Hyun, Hyangsun Han and Hyun-cheol Kim
Remote Sens. 2019, 11(9), 1055; https://doi.org/10.3390/rs11091055 - 4 May 2019
Cited by 8 | Viewed by 3098
Abstract
Satellite remote sensing can be used effectively with a wide coverage and repeatability in large-scale Arctic sea-ice analysis. To produce reliable sea-ice information, satellite remote-sensing methods should be established and validated using accurate field data, but obtaining field data on Arctic sea-ice is [...] Read more.
Satellite remote sensing can be used effectively with a wide coverage and repeatability in large-scale Arctic sea-ice analysis. To produce reliable sea-ice information, satellite remote-sensing methods should be established and validated using accurate field data, but obtaining field data on Arctic sea-ice is very difficult due to limited accessibility. In this situation, digital surface models derived from aerial images can be a good alternative to topographical field data. However, to achieve this, we should discuss an additional issue, i.e., that low-textured surfaces on sea-ice can reduce the matching accuracy of aerial images. The matching performance is dependent on the matching cost and search window size used. Therefore, in order to generate high-quality sea-ice surface models, we first need to examine the influence of matching costs and search window sizes on the matching performance on low-textured sea-ice surfaces. For this reason, in this study, we evaluate the performance of matching costs in relation to changes of the search window size, using acquired aerial images of Arctic sea-ice. The evaluation concerns three factors. The first is the robustness of matching to low-textured surfaces. Matching costs for generating sea-ice surface models should have a high discriminatory power on low-textured surfaces, even with small search windows. To evaluate this, we analyze the accuracy, uncertainty, and optimal window size in terms of template matching. The second is the robustness of positioning to low-textured surfaces. One of the purposes of image matching is to determine the positions of object points that constitute digital surface models. From this point of view, we analyze the accuracy and uncertainty in terms of positioning object points. The last is the processing speed. Since the computation complexity is also an important performance indicator, we analyze the elapsed time for each of the processing steps. The evaluation results showed that the image domain costs were more effective for low-textured surfaces than the frequency domain costs. In terms of matching robustness, the image domain costs showed a better performance, even with smaller search windows. In terms of positioning robustness, the image domain costs also performed better because of the lower uncertainty. Lastly, in terms of processing speed, the PC (phase correlation) of the frequency domain showed the best performance, but the image domain costs, except MI (mutual information), were not far behind. From the evaluation results, we concluded that, among the compared matching costs, ZNCC (zero-mean normalized cross-correlation) is the most effective for sea-ice surface model generation. In addition, we found that it is necessary to adjust search window sizes properly, according to the number of textures required for reliable image matching on sea-ice surfaces, and that various uncertainties due to low-textured surfaces should be considered to determine the positions of object points. Full article
(This article belongs to the Special Issue 3D Reconstruction Based on Aerial and Satellite Imagery)
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20 pages, 17875 KiB  
Article
Hierarchical Clustering-Aligning Framework Based Fast Large-Scale 3D Reconstruction Using Aerial Imagery
by Xiuchuan Xie, Tao Yang, Dongdong Li, Zhi Li and Yanning Zhang
Remote Sens. 2019, 11(3), 315; https://doi.org/10.3390/rs11030315 - 5 Feb 2019
Cited by 6 | Viewed by 3960
Abstract
With extensive applications of Unmanned Aircraft Vehicle (UAV) in the field of remote sensing, 3D reconstruction using aerial images has been a vibrant area of research. However, fast large-scale 3D reconstruction is a challenging task. For aerial image datasets, large scale means that [...] Read more.
With extensive applications of Unmanned Aircraft Vehicle (UAV) in the field of remote sensing, 3D reconstruction using aerial images has been a vibrant area of research. However, fast large-scale 3D reconstruction is a challenging task. For aerial image datasets, large scale means that the number and resolution of images are enormous, which brings significant computational cost to the 3D reconstruction, especially in the process of Structure from Motion (SfM). In this paper, for fast large-scale SfM, we propose a clustering-aligning framework that hierarchically merges partial structures to reconstruct the full scene. Through image clustering, an overlapping relationship between image subsets is established. With the overlapping relationship, we propose a similarity transformation estimation method based on joint camera poses of common images. Finally, we introduce the closed-loop constraint and propose a similarity transformation-based hybrid optimization method to make the merged complete scene seamless. The advantage of the proposed method is a significant efficiency improvement without a marginal loss in accuracy. Experimental results on the Qinling dataset captured over Qinling mountain covering 57 square kilometers demonstrate the efficiency and robustness of the proposed method. Full article
(This article belongs to the Special Issue 3D Reconstruction Based on Aerial and Satellite Imagery)
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