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Remote Sensing
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Applications of Unmanned Aerial Vehicle (UAV) Based Remote Sensing
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Applications of Unmanned Aerial Vehicle (UAV) Based Remote Sensing

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 (10 November 2023) | Viewed by 9513

Special Issue Editors

Prof. Dr. Francisco Agüera-Vega

E-Mail Website
Guest Editor
Department of Engineering, University of Almería, Almería, Spain
Interests: UAV photogrammetry and UAV geomatics applied to natural resources study
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fernando Carvajal-Ramírez

E-Mail Website
Guest Editor
Department of Engineering, University of Almería, 04120 Almería, Spain
Interests: UAV photogrammetry applied to remote sensing of natural resources
Special Issues, Collections and Topics in MDPI journals
Dr. Patricio Martínez-Carricondo

E-Mail Website
Guest Editor
Department of Engineering, University of Almería, Almería, Spain
Interests: UAV photogrammetry applied to remote sensing of natural resources and civil engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photogrammetry based on unmanned aerial vehicles (UAV photogrammetry) is an irruptive technology currently applied to obtain very high resolution digital surface models, orthoimages, and point clouds representing terrain morphology.

Most research issues related to UAV photogrammetry concern the adaptation of precedent classic photogrammetry from aircrafts, satellites, or even close-range photogrammetry to images captured using UAVs.

UAVs introduce new possibilities for photogrammetric projects thanks to their flexibility of route planning, on-board GNSS navigation devices, or inertial data synchronized with shooting.

Photogrammetric software has experimented with parallel development, especially with the implementation of the structure from motion (SfM) algorithm to efficiently manage imagery captured by sensors on-board UAVs, working not only in the visible spectrum, but also the infrared, multispectral, and hyperspectral wavelengths.

For this Special Issue of Remote Sensing, we welcome authors to submit papers related to UAV photogrammetry. The selection of papers for publication will depend on the quality and rigor of research. Specific topics of interest include, but are not limited to, the following:

  • UAV photogrammetry planning, e.g., planning flight routes, surveying campaigns for georeferencing photogrammetric projects, and quality control of UAV photogrammetric products;
  • UAV photogrammetric devices, e.g., differential GNSS devices for reducing the need for ground control points, and on-board UAV photogrammetric sensors;
  • UAV photogrammetric algorithms, e.g., structure from motion, collinearity photogrammetric model, autocorrelation, and orthorectification;
  • UAV photogrammetric products and their applications, e.g., DEM, DTM, point cloud, orthoimages, natural resources morphology, heritage and BIM based on UAV photogrammetric data, civil engineering, and underwater UAV photogrammetry.

Prof. Dr. Francisco Agüera-Vega
Prof. Dr. Fernando Carvajal-Ramírez
Dr. Patricio Martínez-Carricondo
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

  • UAV
  • photogrammetry
  • DEM/DTM
  • structure from motion
  • orthoimage
  • point cloud

Published Papers (5 papers)

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16 pages, 8041 KiB  
Article
YOLO-ViT-Based Method for Unmanned Aerial Vehicle Infrared Vehicle Target Detection
by Xiaofeng Zhao, Yuting Xia, Wenwen Zhang, Chao Zheng and Zhili Zhang
Remote Sens. 2023, 15(15), 3778; https://doi.org/10.3390/rs15153778 - 29 Jul 2023
Cited by 10 | Viewed by 2252
Abstract
The detection of infrared vehicle targets by UAVs poses significant challenges in the presence of complex ground backgrounds, high target density, and a large proportion of small targets, which result in high false alarm rates. To alleviate these deficiencies, a novel YOLOv7-based, multi-scale [...] Read more.
The detection of infrared vehicle targets by UAVs poses significant challenges in the presence of complex ground backgrounds, high target density, and a large proportion of small targets, which result in high false alarm rates. To alleviate these deficiencies, a novel YOLOv7-based, multi-scale target detection method for infrared vehicle targets is proposed, which is termed YOLO-ViT. Firstly, within the YOLOV7-based framework, the lightweight MobileViT network is incorporated as the feature extraction backbone network to fully extract the local and global features of the object and reduce the complexity of the model. Secondly, an innovative C3-PANet neural network structure is delicately designed, which adopts the CARAFE upsampling method to utilize the semantic information in the feature map and improve the model’s recognition accuracy of the target region. In conjunction with the C3 structure, the receptive field will be increased to enhance the network’s accuracy in recognizing small targets and model generalization ability. Finally, the K-means++ clustering method is utilized to optimize the anchor box size, leading to the design of anchor boxes better suited for detecting small infrared targets from UAVs, thereby improving detection efficiency. The present article showcases experimental findings attained through the use of the HIT-UAV public dataset. The results demonstrate that the enhanced YOLO-ViT approach, in comparison to the original method, achieves a reduction in the number of parameters by 49.9% and floating-point operations by 67.9%. Furthermore, the mean average precision (mAP) exhibits an improvement of 0.9% over the existing algorithm, reaching a value of 94.5%, which validates the effectiveness of the method for UAV infrared vehicle target detection. Full article
(This article belongs to the Special Issue Applications of Unmanned Aerial Vehicle (UAV) Based Remote Sensing)
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25 pages, 20321 KiB  
Article
Radiometric Correction with Topography Influence of Multispectral Imagery Obtained from Unmanned Aerial Vehicles
by Agnieszka Jenerowicz, Damian Wierzbicki and Michal Kedzierski
Remote Sens. 2023, 15(8), 2059; https://doi.org/10.3390/rs15082059 - 13 Apr 2023
Cited by 2 | Viewed by 2037
Abstract
This article aims to present the methods of the radiometric correction of multispectral images—a short review of the existing techniques. The role of radiometric correction is essential to many applications, especially in precision farming, forestry, and climate analysis. Moreover, this paper presents a [...] Read more.
This article aims to present the methods of the radiometric correction of multispectral images—a short review of the existing techniques. The role of radiometric correction is essential to many applications, especially in precision farming, forestry, and climate analysis. Moreover, this paper presents a new relative approach, which considers the angle of inclination of the terrain and the angle of incidence of electromagnetic radiation on the imaged objects when obtaining the baseline data. This method was developed for data obtained from low altitudes—for imagery data acquired by sensors mounted on UAV platforms. The paper analyses the effect of the correction on the spectral information, i.e., the compatibility of the spectral reflection characteristics obtained from the image with the spectral reflection characteristics obtained in the field. The developed method of correction for multispectral data obtained from low altitudes allows for the mapping of spectral reflection characteristics to an extent that allows for the classification of terrestrial coverage with an accuracy of over 95%. In addition, it is possible to distinguish objects that are very similar in terms of spectral reflection characteristics. This research presents a new method of correction of each spectral channel obtained by the multispectral camera, increasing the accuracy of the results obtained, e.g., based on SAM coefficients or correlations, but also when distinguishing land cover types during classification. The results are characterized by high accuracy (over 94% in classification). Full article
(This article belongs to the Special Issue Applications of Unmanned Aerial Vehicle (UAV) Based Remote Sensing)
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18 pages, 3476 KiB  
Article
Retrieval of TP Concentration from UAV Multispectral Images Using IOA-ML Models in Small Inland Waterbodies
by Wentong Hu, Jie Liu, He Wang, Donghao Miao, Dongguo Shao and Wenquan Gu
Remote Sens. 2023, 15(5), 1250; https://doi.org/10.3390/rs15051250 - 24 Feb 2023
Cited by 2 | Viewed by 1088
Abstract
Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and [...] Read more.
Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and money is high and it does not meet the needs of spatiotemporal dynamic monitoring. Remote sensing provides an effective tool for TP concentration monitoring spatiotemporally. However, monitoring the TP concentration of small inland waterbodies is challenging for satellite remote sensing due to the inadequate spatial resolution. Recently, unmanned aerial vehicles (UAV) have been applied to quantitatively retrieve the spatiotemporal distribution of TP concentration without the challenges of cloud cover and atmospheric effects. Although state-of-the-art algorithms to retrieve TP concentration have been improved, specific models are only used for specific water quality parameters or regions, and there are no robust and reliable TP retrieval models for small inland waterbodies at this time. To address this issue, six machine learning methods optimized by intelligent optimization algorithms (IOA-ML models) have been developed to quantitatively retrieve TP concentration combined with the reflectance of original bands and selected band combinations of UAV multispectral images. We evaluated the performances of models in terms of coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD). The results showed that the R2 of the six IOA-ML models for training, validation, and test sets were 0.8856–0.984, 0.8054–0.8929, and 0.7462–0.9045, respectively, indicating the methods had high precision and transferability. The extreme gradient boosting optimized by genetic algorithm (GA-XGB) performed best, with the highest precision for the validation and test sets. The spatial distribution of TP concentration of each flight derived from different models had similar distribution characteristics. This paper provides a reference for promoting the intelligent and automatic level of water environment monitoring in small inland waterbodies. Full article
(This article belongs to the Special Issue Applications of Unmanned Aerial Vehicle (UAV) Based Remote Sensing)
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22 pages, 83428 KiB  
Article
Tie Point Matching between Terrestrial and Aerial Images Based on Patch Variational Refinement
by Jianchen Liu, Haoxuan Yin, Baohua Liu and Pingshe Lu
Remote Sens. 2023, 15(4), 968; https://doi.org/10.3390/rs15040968 - 09 Feb 2023
Cited by 2 | Viewed by 1492
Abstract
To produce highly detailed 3D models of architectural scenes, both aerial and terrestrial images are usually captured. However, due to the different viewpoints of each set of images, visual entities in cross-view images show dramatic changes. The perspective distortion makes it difficult to [...] Read more.
To produce highly detailed 3D models of architectural scenes, both aerial and terrestrial images are usually captured. However, due to the different viewpoints of each set of images, visual entities in cross-view images show dramatic changes. The perspective distortion makes it difficult to obtain correspondences between aerial–terrestrial image pairs. To solve this problem, a tie point matching method based on variational patch refinement is proposed. First, aero triangulation is performed on aerial images and terrestrial images, respectively; then, patches are created based on sparse point clouds. Second, the patches are optimized to be close to the surface of the object by variational patch refinement. The perspective distortion and scale difference of the terrestrial and aerial images projected onto the patches are reduced. Finally, tie points between aerial and terrestrial images can be obtained through patch-based matching. Experimental evaluations using four datasets from the ISPRS benchmark datasets and Shandong University of Science and Technology datasets reveal the satisfactory performance of the proposed method in terrestrial–aerial image matching. However, matching time is increased, because point clouds need to be generated. Occlusion in an image, such as that caused by a tree, can influence the generation of point clouds. Therefore, future research directions include the optimization of time complexity and the processing of occluded images. Full article
(This article belongs to the Special Issue Applications of Unmanned Aerial Vehicle (UAV) Based Remote Sensing)
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16 pages, 11674 KiB  
Technical Note
An Integrated Solution of UAV Push-Broom Hyperspectral System Based on Geometric Correction with MSI and Radiation Correction Considering Outdoor Illumination Variation
by Liyao Song, Haiwei Li, Tieqiao Chen, Junyu Chen, Song Liu, Jiancun Fan and Quan Wang
Remote Sens. 2022, 14(24), 6267; https://doi.org/10.3390/rs14246267 - 10 Dec 2022
Cited by 1 | Viewed by 1630
Abstract
The unmanned aerial vehicle (UAV)-borne hyperspectral imaging system has the advantages of high spatial resolution, flexible operation, under-cloud flying, and easy cooperation with ground synchronous tests. Because this platform often flies under clouds, variations in solar illumination lead to irradiance inconsistency between different [...] Read more.
The unmanned aerial vehicle (UAV)-borne hyperspectral imaging system has the advantages of high spatial resolution, flexible operation, under-cloud flying, and easy cooperation with ground synchronous tests. Because this platform often flies under clouds, variations in solar illumination lead to irradiance inconsistency between different rows of hyperspectral images (HSIs). This inconsistency causes errors in radiation correction. In addition, due to the accuracy limitations of the GPS/inertial measurement unit (IMU) and irregular changes in flight platform speed and attitude, HSIs have deformation and drift, which is harmful to the geometric correction and stitching accuracy between flight strips. Consequently, radiation and geometric error limit further applications of large-scale hyperspectral data. To address the above problems, we proposed an integrated solution to acquire and correct UAV-borne hyperspectral images that consist of illumination data acquisition, radiance and geometric correction, HSI, multispectral image (MSI) registration, and multi-strip stitching. We presented an improved three-parameter empirical model based on the illumination correction factor, and it showed that the accuracy of radiation correction considering illumination variation improved, especially in some low signal-to-noise ratio (SNR) bands. In addition, the error of large-scale HSI stitching was controlled within one pixel. Full article
(This article belongs to the Special Issue Applications of Unmanned Aerial Vehicle (UAV) Based Remote Sensing)
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