Special Issue "Unmanned Aerial Vehicles (UAVs) based Remote Sensing"
Quicklinks
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 30 June 2012
Special Issue Editor
Guest Editor
Prof. Dr. Gonzalo Pajares Martinsanz
Dpt. Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain
E-Mail: pajares@fdi.ucm.es
Interests: computer vision; image processing; pattern recognition; spatio-temporal image change detection and track movement; fusion and registering from imaging sensors; superresolution from low-resolution image sensors
Special Issue Information
Dear Colleagues,
The increasing developments in Unmanned Aerial Vehicles (UAVs) platforms and associated sensing technologies, adapted to these platforms, offer a broad range of solutions for different applications related to the acquisition of information about objects, structures or phenomenon at the Earth level, based on observations on the surface seas and oceans and in the atmosphere. The cost-effectiveness compared to manned vehicles makes them attractive, especially considering that UAVs can be equipped with several onboard sensors, including optical and hyperspectral camera-based, Laser, SAR, IMU, GPS among others. Additionally, the increasing technology in communication systems represents a challenge, where fleets of UAVs can collaborate for specific applications. Collaboration is not only limited to UAVs but also to different unmanned or manned ground or marine platforms. The huge amount of data, provided by UAVs, represents a new challenge regarding developments of processing, storage and transmission techniques, where the confluence of multidisciplinary technologies is always welcome. Real and simulated approaches are to be considered.
This special issue will publish papers that address these issues from a broad variety of perspectives. Topics include, but not limited, to:
- UAV systems and autonomy: planes, helicopters, drones
- sensors onboard UAVs: capabilities and technologies
- data processing from UAVs: artificial intelligence and data mining based strategies
- UAV onboard data storage, transmission and retrieval
- communications between UAVs and other systems: wireless, optical
- collaborative strategies and mechanisms between UAVs and other systems: hardware/software architectures including multi-agent systems, protocols and strategies to work together
- UAV applications: agriculture and forestry; humanitarian localization and rescue; security and monitoring surveillance; target tracking including atmospheric phenomena; monitoring of chemical, biological or natural disasters; fire or flooding prevention and early intervention; volcanic activity, earthquake or tsunami early detection; environmental monitoring such as pollution, micro-climates, land use, land cover, change detection; telemetry; 3D terrain and object reconstruction; atmospheric forecast
Prof. Dr. Gonzalo Pajares Martinsanz
Guest Editor
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 500 CHF (Swiss Francs).
English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.
Keywords
- UAV systems
- sensors onboard UAVs
- UAV-based applications
- UAVs collaboration
- UAVs communication
- UAVs data handling
- simulations UAV-based
Published Papers (7 papers)
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Received: 28 September 2011; in revised form: 18 November 2011 / Accepted: 18 November 2011 / Published: 22 November 2011
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Abstract: Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery.
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Received: 29 February 2012; in revised form: 11 April 2012 / Accepted: 13 April 2012 / Published: 20 April 2012
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Abstract: We develop a real-time method to detect and track moving objects (DATMO) from unmanned aerial vehicles (UAVs) using a single camera. To address the challenging characteristics of these vehicles, such as continuous unrestricted pose variation and low-frequency vibrations, new approaches must be developed. The main concept proposed in this work is to create an artificial optical flow field by estimating the camera motion between two subsequent video frames. The core of the methodology consists of comparing this artificial flow with the real optical flow directly calculated from the video feed. The motion of the UAV between frames is estimated with available parallel tracking and mapping techniques that identify good static features in the images and follow them between frames. By comparing the two optical flows, a list of dynamic pixels is obtained and then grouped into dynamic objects. Tracking these dynamic objects through time and space provides a filtering procedure to eliminate spurious events and misdetections. The algorithms have been tested with a quadrotor platform using a commercial camera.
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Received: 12 March 2012; in revised form: 18 April 2012 / Accepted: 19 April 2012 / Published: 27 April 2012
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Abstract: Nowadays wireless sensor netwoks (WSN) technology, wireless communications and digital electronics have made it realistic to produce a large scale miniaturized devices integrating sensing, processing and communication capabilities. The focus of this paper is to present an innovative mobile platform for heterogeneous sensor networks, combined with adaptive methods to optimize the communication architecture for novel potential applications in multimedia and entertainment. In fact, in the near future, some of the applications foreseen for WSNs will employ multi-platform systems with a high number of different devices, which may be completely different in nature, size, computational and energy capabilities, etc. Nowadays, in addition, data collection could be performed by UAV platforms which can be a sink for ground sensors layer, acting essentially as a mobile gateway. In order to maximize the system performances and the network lifespan, the authors propose a recently developed hybrid technique based on evolutionary algorithms. The goal of this procedure is to optimize the communication energy consumption in WSN by selecting the optimal multi-hop routing schemes, with a suitable hybridization of different routing criteria. The proposed approach can be potentially extended and applied to ongoing research projects focused on UAV-based sensing with WSN augmentation and real-time processing for immersive media experiences.
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Received: 29 March 2012; in revised form: 1 May 2012 / Accepted: 2 May 2012 / Published: 9 May 2012
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Abstract: We demonstrate compact, low power, lightweight laser-based sensors for measuring trace gas species in the atmosphere designed specifically for electronic unmanned aerial vehicle (UAV) platforms. The sensors utilize non-intrusive optical sensing techniques to measure atmospheric greenhouse gas concentrations with unprecedented vertical and horizontal resolution (~1 m) within the planetary boundary layer. The sensors are developed to measure greenhouse gas species including carbon dioxide, water vapor and methane in the atmosphere. Key innovations are the coupling of very low power vertical cavity surface emitting lasers (VCSELs) to low power drive electronics and sensitive multi-harmonic wavelength modulation spectroscopic techniques. The overall mass of each sensor is between 1–2 kg including batteries and each one consumes less than 2 W of electrical power. In the initial field testing, the sensors flew successfully onboard a T-Rex Align 700E robotic helicopter and showed a precision of 1% or less for all three trace gas species. The sensors are battery operated and capable of fully automated operation for long periods of time in diverse sensing environments. Laser-based trace gas sensors for UAVs allow for high spatial mapping of local greenhouse gas concentrations in the atmospheric boundary layer where land/atmosphere fluxes occur. The high-precision sensors, coupled to the ease-of-deployment and cost effectiveness of UAVs, provide unprecedented measurement capabilities that are not possible with existing satellite-based and suborbital aircraft platforms.
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Received: 28 March 2012; in revised form: 7 May 2012 / Accepted: 7 May 2012 / Published: 14 May 2012
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Abstract: Unmanned Aerial Vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM) photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP) technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM) required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.
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Received: 28 March 2012; in revised form: 20 April 2012 / Accepted: 4 May 2012 / Published: 18 May 2012
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Abstract: Unmanned aerial vehicles (UAVs) represent a quickly evolving technology, broadening the availability of remote sensing tools to small-scale research groups across a variety of scientific fields. Development of UAV platforms requires broad technical skills covering platform development, data post-processing, and image analysis. UAV development is constrained by a need to balance technological accessibility, flexibility in application and quality in image data. In this study, the quality of UAV imagery acquired by a miniature 6-band multispectral imaging sensor was improved through the application of practical image-based sensor correction techniques. Three major components of sensor correction were focused upon: noise reduction, sensor-based modification of incoming radiance, and lens distortion. Sensor noise was reduced through the use of dark offset imagery. Sensor modifications through the effects of filter transmission rates, the relative monochromatic efficiency of the sensor and the effects of vignetting were removed through a combination of spatially/spectrally dependent correction factors. Lens distortion was reduced through the implementation of the Brown–Conrady model. Data post-processing serves dual roles in data quality improvement, and the identification of platform limitations and sensor idiosyncrasies. The proposed corrections improve the quality of the raw multispectral imagery, facilitating subsequent quantitative image analysis.
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Received: 14 March 2012; in revised form: 14 May 2012 / Accepted: 17 May 2012 / Published: 25 May 2012
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Abstract: We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m2) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points perm2, to 0.15mwhen the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.
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Last update: 18 May 2012
