A Video Geo-Location and Image Enhancement Tool for Small Unmanned Air Vehicles (UAVs)

Gibbins, Danny; Roberts, Peter; Swierkowski, Leszek

doi:10.1109/issnip.2004.1417506

Cited by 22 publications

(11 citation statements)

References 6 publications

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“…The first one involves finding an intersection of the camera line of sight (LOS) with a local Earth surface [18]. This approach is often referred to as a "geolocation via ray intersection."…”

Section: Doi: 102514/133206mentioning

confidence: 99%

Vision-Based Tracking and Motion Estimation for Moving Targets Using Unmanned Air Vehicles

Dobrokhodov

Kaminer

Jones

et al. 2008

Journal of Guidance, Control, and Dynamics

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Section: Doi: 102514/133206mentioning

confidence: 99%

Vision-Based Tracking and Motion Estimation for Moving Targets Using Unmanned Air Vehicles

Dobrokhodov

Kaminer

Jones

et al. 2008

Journal of Guidance, Control, and Dynamics

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“…The simplest is to use the vehicle's pose provided by onboard sensors to project the image coordinate as a ray in world coordinates [1] that may then be intersected with a terrain model [2] to produce a location on the ground. Another strategy is registration of live video to previously acquired satellite imagery [3], which provides a direct measurement of a viewable object's world location.…”

Section: Related Workmentioning

confidence: 99%

Efficient target geolocation by highly uncertain small air vehicles

Grocholsky

Dille

Nuske

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Geolocation of a ground object or target of interest from live video is a common task required of small and micro unmanned aerial vehicles (SUAVs and MAVs) in surveillance and rescue applications. However, such vehicles commonly carry low-cost and light-weight sensors providing poor bandwidth and accuracy whose contribution to observations is nonlinear, resulting in poor geolocation performance by standard techniques. This paper proposes the application of an efficient over-parameterized state representation to the problem of geolocation that is able to handle large, time-varying, and non-Gaussian sensor error to produce better geolocation estimates than typical approaches and which provides computing and communication benefits in applications such as predictive control and distributed collaboration. We evaluate our filter on real flight data, demonstrating its ability to efficiently produce a solution with tight confidence bounds given highly uncertain data.

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“…Compared with satellite remote sensing monitoring system, air monitoring, marine monitoring and other traditional platforms, UAV remote sensing systems have the characteristics of low cost, simple operation, fast response, efficient operation, can acquire highresolution aerial images [2] .…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Image Segmentation Based on Fuzzy C-means Algorithm

Sun¹,

Ma²,

Zhao³

et al. 2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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Abstract-Oil spill may cause serious pollution of the marine environment. Unmanned aerial vehicles remote sensing system can be used to monitor oil spill conditions. In order to identify the oil spill area on aerial image accurately, the first step is oil spill region segmentation. The paper presents an image segmentation method of oil spill area based on fuzzy C-means Algorithm. Firstly, according to the color characteristics of the oil, the paper selects YCbCr color space as the feature space. Then, the paper uses fuzzy clustering algorithm to divide the color feature space. Finally, according to oil color model, the paper selects clustering result as the segmentation results of oil spill area. Experiment show that the proposed algorithm's accuracy for oil region segmentation of calibration attain to 95 percent.

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A Video Geo-Location and Image Enhancement Tool for Small Unmanned Air Vehicles (UAVs)

Cited by 22 publications

References 6 publications

Vision-Based Tracking and Motion Estimation for Moving Targets Using Unmanned Air Vehicles

Vision-Based Tracking and Motion Estimation for Moving Targets Using Unmanned Air Vehicles

Efficient target geolocation by highly uncertain small air vehicles

Oil Spill Image Segmentation Based on Fuzzy C-means Algorithm

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