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Special Issue "Recent Trends in UAV Remote Sensing"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2016)

Special Issue Editors

Guest Editor
Dr. Farid Melgani

Department of Information Engineering and Computer Science, University of Trento, I-38123 Trento, Italy
Website | E-Mail
Interests: computer vision; image processing and analysis; machine learning; pattern recognition; UAV
Guest Editor
Dr. Francesco Nex

Department of Earth Observation Science (EOS), ITC Faculty, University of Twente PO Box 217, 7500 AE, Enschede, The Netherlands
Website | E-Mail
Interests: geometric and radiometric sensors; sensor fusion; calibration of imageries; signal/image processing; mission planning; navigation and position/orientation; Machine learning; Simultaneous localization and mapping; Regulations, and economic impact; agriculture; geosciences; urban area; architecture; monitoring/change detection; education

Special Issue Information

Dear Colleagues,

Nowadays, Unmanned Aerial Vehicles (UAVs) are involved in a wide range of remote sensing applications. UAVs are rapid, efficient and flexible acquisition systems. They represent a valid alternative or a complementary solution to satellite or airborne sensors especially for extremely high resolution acquisitions on small or inaccessible areas. Thanks to their timely, cheap and extremely rich data acquisition capacity with respect to other acquisition systems, UAVs are emerging as innovative and cost-effective devices to perform numerous urban and environmental survey tasks.

This Special Issue aims at collecting new developments and methodologies, best practices and applications of UAVs for remote sensing. We welcome submissions which provide the community with the most recent advancements on all aspects of UAV remote sensing, including but not limited to:

  • Data processing and photogrammetry
  • Data analysis (image classification, feature extraction, target detection, change detection, biophysical parameter estimation, etc.)
  • Platforms and new sensors on board (multispectral, hyperspectral, thermal, lidar, SAR, gas or radioactivity sensors, etc.)
  • Data fusion: integration of UAV imagery with satellite, aerial or terrestrial data, integration of heterogeneous data captured by UAVs
  • Real time processing / collaborative and fleet of UAVs applied to remote sensing
  • Onboard data storage and transmission
  • Applications (3D mapping, urban monitoring, precision farming, forestry, disaster prevention, assessment and monitoring, search and rescue, security, archaeology, industrial plant inspection, etc.)
  • Review of national and international regulations
  • Any use of UAVs related to remote sensing

Farid Melgani
Francesco Nex
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 monthly 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 1600 CHF (Swiss Francs).

Published Papers (8 papers)

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Research

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Open AccessFeature PaperArticle First Results of a Tandem Terrestrial-Unmanned Aerial mapKITE System with Kinematic Ground Control Points for Corridor Mapping
Remote Sens. 2017, 9(1), 60; doi:10.3390/rs9010060
Received: 30 November 2016 / Revised: 1 January 2017 / Accepted: 4 January 2017 / Published: 11 January 2017
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Abstract
In this article, we report about the first results of the mapKITE system, a tandem terrestrial-aerial concept for geodata acquisition and processing, obtained in corridor mapping missions. The system combines an Unmanned Aerial System (UAS) and a Terrestrial Mobile Mapping System (TMMS) operated
[...] Read more.
In this article, we report about the first results of the mapKITE system, a tandem terrestrial-aerial concept for geodata acquisition and processing, obtained in corridor mapping missions. The system combines an Unmanned Aerial System (UAS) and a Terrestrial Mobile Mapping System (TMMS) operated in a singular way: real-time waypoints are computed from the TMMS platform and sent to the UAS in a follow-me scheme. This approach leads to a simultaneous acquisition of aerial-plus-ground geodata and, moreover, opens the door to an advanced post-processing approach for sensor orientation. The current contribution focuses on analysing the impact of the new, dynamic Kinematic Ground Control Points (KGCPs), which arise inherently from the mapKITE paradigm, as an alternative to conventional, costly Ground Control Points (GCPs). In the frame of a mapKITE campaign carried out in June 2016, we present results entailing sensor orientation and calibration accuracy assessment through ground check points, and precision and correlation analysis of self-calibration parameters’ estimation. Conclusions indicate that the mapKITE concept eliminates the need for GCPs when using only KGCPs plus a couple of GCPs at each corridor end, achieving check point horizontal accuracy of μ E , N 1.7 px (3.4 cm) and μ h 4.3 px (8.6 cm). Since obtained from a simplified version of the system, these preliminary results are encouraging from a future perspective. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Optimizing Multiple Kernel Learning for the Classification of UAV Data
Remote Sens. 2016, 8(12), 1025; doi:10.3390/rs8121025
Received: 26 October 2016 / Revised: 8 December 2016 / Accepted: 9 December 2016 / Published: 16 December 2016
PDF Full-text (3249 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned Aerial Vehicles (UAVs) are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from
[...] Read more.
Unmanned Aerial Vehicles (UAVs) are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL) for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM). A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland
Remote Sens. 2016, 8(11), 968; doi:10.3390/rs8110968
Received: 27 August 2016 / Revised: 14 November 2016 / Accepted: 16 November 2016 / Published: 23 November 2016
PDF Full-text (3301 KB) | HTML Full-text | XML Full-text
Abstract
Application of 3D data derived from images captured using unmanned aerial vehicles (UAVs) in forest biomass estimation has shown great potential in reducing costs and improving the estimates. However, such data have never been tested in miombo woodlands. UAV-based biomass estimation relies on
[...] Read more.
Application of 3D data derived from images captured using unmanned aerial vehicles (UAVs) in forest biomass estimation has shown great potential in reducing costs and improving the estimates. However, such data have never been tested in miombo woodlands. UAV-based biomass estimation relies on the availability of reliable digital terrain models (DTMs). The main objective of this study was to evaluate application of 3D data derived from UAV imagery in biomass estimation and to compare impacts of DTMs generated based on different methods and parameter settings. Biomass was modeled using data acquired from 107 sample plots in a forest reserve in miombo woodlands of Malawi. The results indicated that there are no significant differences (p = 0.985) between tested DTMs except for that based on shuttle radar topography mission (SRTM). A model developed using unsupervised ground filtering based on a grid search approach, had the smallest root mean square error (RMSE) of 46.7% of a mean biomass value of 38.99 Mg·ha−1. Amongst the independent variables, maximum canopy height (Hmax) was the most frequently selected. In addition, all models included spectral variables incorporating the three color bands red, green and blue. The study has demonstrated that UAV acquired image data can be used in biomass estimation in miombo woodlands using automatically generated DTMs. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle An Image-Based Approach for the Co-Registration of Multi-Temporal UAV Image Datasets
Remote Sens. 2016, 8(9), 779; doi:10.3390/rs8090779
Received: 4 July 2016 / Revised: 3 September 2016 / Accepted: 13 September 2016 / Published: 21 September 2016
PDF Full-text (14744 KB) | HTML Full-text | XML Full-text
Abstract
During the past years, UAVs (Unmanned Aerial Vehicles) became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the
[...] Read more.
During the past years, UAVs (Unmanned Aerial Vehicles) became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the acquired multi-temporal data still remains an open issue. Most UAVs are not able to provide accurate direct image georeferencing and the co-registration process is mostly performed with the manual introduction of ground control points (GCPs), which is time consuming, costly and sometimes not possible at all. A new technique to automate the co-registration of multi-temporal high resolution image blocks without the use of GCPs is investigated in this paper. The image orientation is initially performed on a reference epoch and the registration of the following datasets is achieved including some anchor images from the reference data. The interior and exterior orientation parameters of the anchor images are then fixed in order to constrain the Bundle Block Adjustment of the slave epoch to be aligned with the reference one. The study involved the use of two different datasets acquired over a construction site and a post-earthquake damaged area. Different tests have been performed to assess the registration procedure using both a manual and an automatic approach for the selection of anchor images. The tests have shown that the procedure provides results comparable to the traditional GCP-based strategy and both the manual and automatic selection of the anchor images can provide reliable results. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
Remote Sens. 2016, 8(9), 724; doi:10.3390/rs8090724
Received: 3 May 2016 / Revised: 22 August 2016 / Accepted: 29 August 2016 / Published: 1 September 2016
PDF Full-text (2614 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs) provides aerial images with sub-decimetre resolution and offers a potential
[...] Read more.
Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs) provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m × 100 m) differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest) using eCognition®. The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated) the desired level of thematic detail and the required accuracy for the mapping task needs to be considered. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Quantifying the Effect of Aerial Imagery Resolution in Automated Hydromorphological River Characterisation
Remote Sens. 2016, 8(8), 650; doi:10.3390/rs8080650
Received: 5 June 2016 / Revised: 23 July 2016 / Accepted: 3 August 2016 / Published: 10 August 2016
PDF Full-text (7397 KB) | HTML Full-text | XML Full-text
Abstract
Existing regulatory frameworks aiming to improve the quality of rivers place hydromorphology as a key factor in the assessment of hydrology, morphology and river continuity. The majority of available methods for hydromorphological characterisation rely on the identification of homogeneous areas (i.e., features) of
[...] Read more.
Existing regulatory frameworks aiming to improve the quality of rivers place hydromorphology as a key factor in the assessment of hydrology, morphology and river continuity. The majority of available methods for hydromorphological characterisation rely on the identification of homogeneous areas (i.e., features) of flow, vegetation and substrate. For that purpose, aerial imagery is used to identify existing features through either visual observation or automated classification techniques. There is evidence to believe that the success in feature identification relies on the resolution of the imagery used. However, little effort has yet been made to quantify the uncertainty in feature identification associated with the resolution of the aerial imagery. This paper contributes to address this gap in knowledge by contrasting results in automated hydromorphological feature identification from unmanned aerial vehicles (UAV) aerial imagery captured at three resolutions (2.5 cm, 5 cm and 10 cm) along a 1.4 km river reach. The results show that resolution plays a key role in the accuracy and variety of features identified, with larger identification errors observed for riffles and side bars. This in turn has an impact on the ecological characterisation of the river reach. The research shows that UAV technology could be essential for unbiased hydromorphological assessment. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Stable Imaging and Accuracy Issues of Low-Altitude Unmanned Aerial Vehicle Photogrammetry Systems
Remote Sens. 2016, 8(4), 316; doi:10.3390/rs8040316
Received: 16 January 2016 / Revised: 25 March 2016 / Accepted: 6 April 2016 / Published: 9 April 2016
PDF Full-text (8985 KB) | HTML Full-text | XML Full-text
Abstract
Stable imaging of an unmanned aerial vehicle (UAV) photogrammetry system is an important issue that affects the data processing and application of the system. Compared with traditional aerial images, the large rotation of roll, pitch, and yaw angles of UAV images decrease image
[...] Read more.
Stable imaging of an unmanned aerial vehicle (UAV) photogrammetry system is an important issue that affects the data processing and application of the system. Compared with traditional aerial images, the large rotation of roll, pitch, and yaw angles of UAV images decrease image quality and result in image deformation, thereby affecting the ground resolution, overlaps, and the consistency of the stereo models. These factors also cause difficulties in automatic tie point matching, image orientation, and accuracy of aerial triangulation (AT). The issues of large-angle photography of UAV photogrammetry system are discussed and analyzed quantitatively in this paper, and a simple and lightweight three-axis stabilization platform that works with a low-precision integrated inertial navigation system and a three-axis mechanical platform is used to reduce this problem. An experiment was carried out with an airship as the flight platform. Another experimental dataset, which was acquired by the same flight platform without a stabilization platform, was utilized for a comparative test. Experimental results show that the system can effectively isolate the swing of the flying platform. To ensure objective and reliable results, another group of experimental datasets, which were acquired using a fixed-wing UAV platform, was also analyzed. Statistical results of the experimental datasets confirm that stable imaging of a UAV platform can help improve the quality of aerial photography imagery and the accuracy of AT, and potentially improve the application of images acquired by a UAV. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Review

Jump to: Research

Open AccessReview Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping
Remote Sens. 2016, 8(8), 689; doi:10.3390/rs8080689
Received: 30 June 2016 / Revised: 3 August 2016 / Accepted: 11 August 2016 / Published: 22 August 2016
Cited by 1 | PDF Full-text (5051 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to
[...] Read more.
Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step—including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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