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Remote Sensing
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UAV Photogrammetry for 3D Modeling
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UAV Photogrammetry for 3D Modeling

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

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 6080

Special Issue Editors

Prof. Dr. Efstratios Stylianidis

E-Mail Website
Guest Editor
School of Spatial Planning and Development, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Interests: photogrammetry; geomatics; cultural heritage; documentation
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Javier Sánchez Aparicio

E-Mail Website
Guest Editor
Department of Construction and Technology in Architecture (DCTA), Escuela Técnica Superior de Arquitectura de Madrid (ETSAM), Universidad Politécnica de Madrid, Madrid, Spain
Interests: cultural heritage; geomatics; laser scanning; photogrammetry; diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) have been extensively used in many application areas. They are able to carry several sensors, capture aerial imagery, or even acquire point clouds depending on the application area and the sensor equipped on the UAV.

This Special Issue aspires to focus on the latest advances in 3D modeling issues, targeting both natural and built environments. This Special Issue seeks high-quality papers that explore all the potentialities offered by these platforms and the latest developments in data acquisition, processing, and 3D modeling in a wide spectrum of applications.

I would like to invite you to contribute to this Special Issue “UAV Photogrammetry for 3D Modeling” by submitting articles concerning your recent research, experimental work, reviews, and/or case studies related to the topic encapsulated by the title. Contributions may be on UAV image processing methods for photogrammetric applications, mapping and 3D modeling issues, and any other aspects related to the Special Issue theme.

This Special Issue provides a scientific basis for any researcher and professional in the UAV ecosystem to make the best possible use and presentation of technological developments, both in hardware and software, toward using this incredible ‘infrastructure’ in the entire 3D modeling workflow.

You may choose our Joint Special Issue in Drones.

Prof. Dr. Efstratios Stylianidis
Prof. Dr. Luis Javier Sánchez-Aparicio
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

  • Unmanned aerial vehicles, drones
  • 3D modeling
  • Photogrammetry, computer vision, and remote sensing
  • Mapping, surveying
  • Sensors
  • Natural environment, built environment

Related Special Issue

Published Papers (3 papers)

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Research

25 pages, 6713 KiB  
Article
Efficient and Accurate Hierarchical SfM Based on Adaptive Track Selection for Large-Scale Oblique Images
by Yubin Liang, Yang Yang, Xiaochang Fan and Tiejun Cui
Remote Sens. 2023, 15(5), 1374; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051374 - 28 Feb 2023
Cited by 4 | Viewed by 1269
Abstract
Image-based 3D modeling has been widely used in many areas. Structure from motion is the key to image-based reconstruction. However, the rapid growth of data poses challenges to current SfM solutions. A hierarchical SfM reconstruction methodology for large-scale oblique images is proposed. Firstly, [...] Read more.
Image-based 3D modeling has been widely used in many areas. Structure from motion is the key to image-based reconstruction. However, the rapid growth of data poses challenges to current SfM solutions. A hierarchical SfM reconstruction methodology for large-scale oblique images is proposed. Firstly, match pairs are selected using positioning and orientation (POS) data and the terrain of the survey area. Then, images are divided to image groups by traversing the selected match pairs. After pairwise image matching, tracks are decimated using an adaptive track selection method. Thirdly, submaps are reconstructed from the image groups in parallel based on incremental SfM in the object space. A novel method based on statistics of the positional difference between common tracks is proposed to detect the outliers in submap merging. Finally, the reconstructed submaps are incrementally merged and optimized. The proposed methodology was used on a large oblique image set. The proposed methodology was compared with the state-of-the-art image-based reconstruction systems COLMAP and Metashape for SfM reconstruction. Experimental results show that the proposed methodology achieved the highest accuracy on the experimental dataset, i.e., about 22.37, and 3.52 times faster than COLMAP and Metashape, respectively. The experimental results demonstrate that the proposed hierarchical SfM methodology is accurate and efficient for large-scale oblique images. Full article
(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)
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19 pages, 3194 KiB  
Article
Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid
by Li Zhu, Zihao Xie, Jing Luo, Yuhang Qi, Liman Liu and Wenbing Tao
Remote Sens. 2021, 13(22), 4610; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224610 - 16 Nov 2021
Cited by 5 | Viewed by 1969
Abstract
Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is [...] Read more.
Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet. Full article
(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)
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19 pages, 29646 KiB  
Article
A Parallel Method for Open Hole Filling in Large-Scale 3D Automatic Modeling Based on Oblique Photography
by Fei Wang, Zhendong Liu, Hongchun Zhu and Pengda Wu
Remote Sens. 2021, 13(17), 3512; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173512 - 04 Sep 2021
Cited by 3 | Viewed by 1765
Abstract
Common methods of filling open holes first reaggregate them into closed holes and then use a closed hole filling method to repair them. These methods have problems such as long calculation times, high memory consumption, and difficulties in filling large-area open holes. Hence, [...] Read more.
Common methods of filling open holes first reaggregate them into closed holes and then use a closed hole filling method to repair them. These methods have problems such as long calculation times, high memory consumption, and difficulties in filling large-area open holes. Hence, this paper proposes a parallel method for open hole filling in large-scale 3D automatic modeling. First, open holes are automatically identified and divided into two categories (internal and external). Second, the hierarchical relationships between the open holes are calculated in accordance with the adjacency relationships between partitioning cells, and the open holes are filled through propagation from the outer level to the inner level with topological closure and height projection transformation. Finally, the common boundaries between adjacent open holes are smoothed based on the Laplacian algorithm to achieve natural transitions between partitioning cells. Oblique photography data from an area of 28 km2 in Dongying, Shandong, were used for validation. The experimental results reveal the following: (i) Compared to the Han method, the proposed approach has a 12.4% higher filling success rate for internal open holes and increases the filling success rate for external open holes from 0% to 100%. (ii) Concerning filling efficiency, the Han method can achieve hole filling only in a small area, whereas with the proposed method, the size of the reconstruction area is not restricted. The time and memory consumption are improved by factors of approximately 4–5 and 7–21, respectively. (iii) In terms of filling accuracy, the two methods are basically the same. Full article
(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)
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