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Special Issue "Unmanned Aerial Vehicles (UAVs) based Remote Sensing"

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

Deadline for manuscript submissions: closed (30 June 2012)

Special Issue Editor

Guest Editor
Prof. Dr. Gonzalo Pajares Martinsanz (Website)

Department Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain
Phone: +34.1.3947546
Interests: computer vision; image processing; pattern recognition; 3D image reconstruction, 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

Keywords

  • UAV systems
  • sensors onboard UAVs
  • UAV-based applications
  • UAVs collaboration
  • UAVs communication
  • UAVs data handling
  • simulations UAV-based

Published Papers (14 papers)

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Research

Open AccessArticle Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco
Remote Sens. 2012, 4(11), 3390-3416; doi:10.3390/rs4113390
Received: 30 August 2012 / Revised: 16 October 2012 / Accepted: 17 October 2012 / Published: 7 November 2012
Cited by 62 | PDF Full-text (2203 KB) | HTML Full-text | XML Full-text
Abstract
This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with [...] Read more.
This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV’s log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
Open AccessArticle Model-Free Trajectory Optimisation for Unmanned Aircraft Serving as Data Ferries for Widespread Sensors
Remote Sens. 2012, 4(10), 2971-3005; doi:10.3390/rs4102971
Received: 21 July 2012 / Revised: 18 September 2012 / Accepted: 18 September 2012 / Published: 9 October 2012
Cited by 7 | PDF Full-text (1064 KB) | HTML Full-text | XML Full-text
Abstract
Given multiple widespread stationary data sources such as ground-based sensors, an unmanned aircraft can fly over the sensors and gather the data via a wireless link. Performance criteria for such a network may incorporate costs such as trajectory length for the aircraft [...] Read more.
Given multiple widespread stationary data sources such as ground-based sensors, an unmanned aircraft can fly over the sensors and gather the data via a wireless link. Performance criteria for such a network may incorporate costs such as trajectory length for the aircraft or the energy required by the sensors for radio transmission. Planning is hampered by the complex vehicle and communication dynamics and by uncertainty in the locations of sensors, so we develop a technique based on model-free learning. We present a stochastic optimisation method that allows the data-ferrying aircraft to optimise data collection trajectories through an unknown environment in situ, obviating the need for system identification. We compare two trajectory representations, one that learns near-optimal trajectories at low data requirements but that fails at high requirements, and one that gives up some performance in exchange for a data collection guarantee. With either encoding the ferry is able to learn significantly improved trajectories compared with alternative heuristics. To demonstrate the versatility of the model-free learning approach, we also learn a policy to minimise the radio transmission energy required by the sensor nodes, allowing prolonged network lifetime. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
Open AccessArticle Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle
Remote Sens. 2012, 4(9), 2736-2752; doi:10.3390/rs4092736
Received: 20 July 2012 / Revised: 6 September 2012 / Accepted: 10 September 2012 / Published: 17 September 2012
Cited by 24 | PDF Full-text (2984 KB) | HTML Full-text | XML Full-text
Abstract
In the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment [...] Read more.
In the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 m (based on RMSE) with a flying height of 344 m above ground level (AGL). Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
Open AccessArticle Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle
Remote Sens. 2012, 4(7), 2076-2111; doi:10.3390/rs4072076
Received: 17 May 2012 / Revised: 2 July 2012 / Accepted: 4 July 2012 / Published: 12 July 2012
Cited by 11 | PDF Full-text (1660 KB) | HTML Full-text | XML Full-text
Abstract
This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new [...] Read more.
This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
Open AccessArticle Radiation Mapping in Post-Disaster Environments Using an Autonomous Helicopter
Remote Sens. 2012, 4(7), 1995-2015; doi:10.3390/rs4071995
Received: 7 May 2012 / Revised: 2 July 2012 / Accepted: 2 July 2012 / Published: 5 July 2012
Cited by 14 | PDF Full-text (5282 KB) | HTML Full-text | XML Full-text
Abstract
Recent events have highlighted the need for unmanned remote sensing in dangerous areas, particularly where structures have collapsed or explosions have occurred, to limit hazards to first responders and increase their efficiency in planning response operations. In the case of the Fukushima [...] Read more.
Recent events have highlighted the need for unmanned remote sensing in dangerous areas, particularly where structures have collapsed or explosions have occurred, to limit hazards to first responders and increase their efficiency in planning response operations. In the case of the Fukushima nuclear reactor explosion, an unmanned helicopter capable of obtaining overhead images, gathering radiation measurements, and mapping both the structural and radiation content of the environment would have given the response team invaluable data early in the disaster, thereby allowing them to understand the extent of the damage and areas where dangers to personnel existed. With this motivation, the Unmanned Systems Lab at Virginia Tech has developed a remote sensing system for radiation detection and aerial imaging using a 90 kg autonomous helicopter and sensing payloads for the radiation detection and imaging operations. The radiation payload, which is the sensor of focus in this paper, consists of a scintillating type detector with associated software and novel search algorithms to rapidly and effectively map and locate sources of high radiation intensity. By incorporating this sensing technology into an unmanned aerial vehicle system, crucial situational awareness can be gathered about a post-disaster environment and response efforts can be expedited. This paper details the radiation mapping and localization capabilities of this system as well as the testing of the various search algorithms using simulated radiation data. The various components of the system have been flight tested over a several-year period and a new production flight platform has been built to enhance reliability and maintainability. The new system is based on the Aeroscout B1-100 helicopter platform, which has a one-hour flight endurance and uses a COFDM radio system that gives the helicopter an effective range of 7 km. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
Open AccessArticle Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use
Remote Sens. 2012, 4(6), 1671-1692; doi:10.3390/rs4061671
Received: 7 April 2012 / Revised: 1 June 2012 / Accepted: 4 June 2012 / Published: 8 June 2012
Cited by 90 | PDF Full-text (1061 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned Aircraft Systems (UAS) have evolved rapidly over the past decade driven primarily by military uses, and have begun finding application among civilian users for earth sensing reconnaissance and scientific data collection purposes. Among UAS, promising characteristics are long flight duration, improved [...] Read more.
Unmanned Aircraft Systems (UAS) have evolved rapidly over the past decade driven primarily by military uses, and have begun finding application among civilian users for earth sensing reconnaissance and scientific data collection purposes. Among UAS, promising characteristics are long flight duration, improved mission safety, flight repeatability due to improving autopilots, and reduced operational costs when compared to manned aircraft. The potential advantages of an unmanned platform, however, depend on many factors, such as aircraft, sensor types, mission objectives, and the current UAS regulatory requirements for operations of the particular platform. The regulations concerning UAS operation are still in the early development stages and currently present significant barriers to entry for scientific users. In this article we describe a variety of platforms, as well as sensor capabilities, and identify advantages of each as relevant to the demands of users in the scientific research sector. We also briefly discuss the current state of regulations affecting UAS operations, with the purpose of informing the scientific community about this developing technology whose potential for revolutionizing natural science observations is similar to those transformations that GIS and GPS brought to the community two decades ago. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
Open AccessArticle Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery
Remote Sens. 2012, 4(6), 1573-1599; doi:10.3390/rs4061573
Received: 9 April 2012 / Revised: 22 May 2012 / Accepted: 25 May 2012 / Published: 30 May 2012
Cited by 72 | PDF Full-text (2145 KB) | HTML Full-text | XML Full-text
Abstract
Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV) have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing) is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS) are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from ~50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion) can be monitored. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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Open AccessArticle Development of a UAV-LiDAR System with Application to Forest Inventory
Remote Sens. 2012, 4(6), 1519-1543; doi:10.3390/rs4061519
Received: 14 March 2012 / Revised: 14 May 2012 / Accepted: 17 May 2012 / Published: 25 May 2012
Cited by 56 | PDF Full-text (13408 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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Open AccessArticle Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing
Remote Sens. 2012, 4(5), 1462-1493; doi:10.3390/rs4051462
Received: 28 March 2012 / Revised: 20 April 2012 / Accepted: 4 May 2012 / Published: 18 May 2012
Cited by 26 | PDF Full-text (15527 KB) | HTML Full-text | XML Full-text
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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Open AccessArticle An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds
Remote Sens. 2012, 4(5), 1392-1410; doi:10.3390/rs4051392
Received: 28 March 2012 / Revised: 7 May 2012 / Accepted: 7 May 2012 / Published: 14 May 2012
Cited by 84 | PDF Full-text (6323 KB) | HTML Full-text | XML Full-text
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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Open AccessArticle Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles
Remote Sens. 2012, 4(5), 1355-1368; doi:10.3390/rs4051355
Received: 29 March 2012 / Revised: 1 May 2012 / Accepted: 2 May 2012 / Published: 9 May 2012
Cited by 23 | PDF Full-text (693 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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Open AccessArticle Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications
Remote Sens. 2012, 4(5), 1146-1161; doi:10.3390/rs4051146
Received: 12 March 2012 / Revised: 18 April 2012 / Accepted: 19 April 2012 / Published: 27 April 2012
Cited by 18 | PDF Full-text (370 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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Open AccessArticle A Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera
Remote Sens. 2012, 4(4), 1090-1111; doi:10.3390/rs4041090
Received: 29 February 2012 / Revised: 11 April 2012 / Accepted: 13 April 2012 / Published: 20 April 2012
Cited by 26 | PDF Full-text (12004 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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Open AccessArticle Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments
Remote Sens. 2011, 3(11), 2529-2551; doi:10.3390/rs3112529
Received: 28 September 2011 / Revised: 18 November 2011 / Accepted: 18 November 2011 / Published: 22 November 2011
Cited by 50 | PDF Full-text (3167 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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