2016
DOI: 10.5194/isprs-archives-xli-b1-941-2016
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Toward Real Time Uavs’ Image Mosaicking

Abstract: ABSTRACT:Anyone knows that sudden catastrophes can instantly do great damage. Fast and accurate acquisition of catastrophe information is an essential task for minimize life and property damage. Compared with other ways of catastrophe data acquisition, UAV based platforms can optimize time, cost and accuracy of the data acquisition, as a result UAVs' data has become the first choice in such condition. In this paper, a novel and fast strategy is proposed for registering and mosaicking of UAVs' image data. First… Show more

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Cited by 6 publications
(1 citation statement)
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“…Years later drones have been used to analyse the effects of a landslide in Tibet [10], comparing the profile of the terrain before and after the catastrophe. More recently, Mehrdad et al [11] proposed a technique specifically applicable to natural disasters that is able to quickly and efficiently combine aerial images, and which has direct applicability in the case of using a swarm of drones to obtain such images. However, if we are attempting to perform complex tasks in a short time, such as assessing the effects of a natural disaster and searching for survivors, the deployment of swarms of UAVs is a very interesting alternative.…”
Section: A Uavs In Natural Disastersmentioning
confidence: 99%
“…Years later drones have been used to analyse the effects of a landslide in Tibet [10], comparing the profile of the terrain before and after the catastrophe. More recently, Mehrdad et al [11] proposed a technique specifically applicable to natural disasters that is able to quickly and efficiently combine aerial images, and which has direct applicability in the case of using a swarm of drones to obtain such images. However, if we are attempting to perform complex tasks in a short time, such as assessing the effects of a natural disaster and searching for survivors, the deployment of swarms of UAVs is a very interesting alternative.…”
Section: A Uavs In Natural Disastersmentioning
confidence: 99%