a rational function model based geo-positioning method for satellite images without using ground control points

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ID: 233538
2018
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Abstract
Earth observation satellites with various spatial, spectral and temporal resolutions provide an invaluable means for mapping and monitoring the Earth’s environments. With the increasing demand of satellite images for remote and harsh environments and nature disaster areas such as earthquake, flooding, bushfires and other emergent events, quickly geo-positioning those images without using ground control points (GCPs) is much preferable and desirable. Built on the previously developed Spatial Triangulated Network (STN) concept by the first author, this paper presents a Rational Function Model (RFM) based geo-positioning method utilizing some pre-orientated image(s) as reference, instead of using GCPs. The experimental results indicate that the RFM method is more sensitive to the base-height ratio in the vertical accuracy than the physical model based geo-positioning method which was also developed by the first author. Compared to the traditional RFM based block adjustment using GCPs, the proposed RFM based method without GCP (using orientated images instead) can achieve similar accuracies when more than one orientated image, which have reasonable strong geometric relationships with the new images, are introduced into the proposed RFM based method. The proposed method is applicable to the scenarios in which geo-positioning is required for those new satellite images that only have RFM and no GCPs available, but where there exists some orientated images covering the same region.
Reference Key
ma2018remotea Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhenling Ma;Wei Song;Junping Deng;Jiali Wang;Cancan Cui
Journal Journal of pharmacological sciences
Year 2018
DOI
10.3390/rs10020182
URL
http://www.mdpi.com/2072-4292/10/2/182
Keywords
vegetation index

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