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.
Highlights
Earth observation satellites provide an invaluable means for mapping and monitoring the Earth’s environment through various spatial, spectral and temporal resolutions, such as mapping rivers and vegetation for environmental applications, creating feature and elevation maps for topography mapping, mapping coastlines for renewable natural resources applications, and so on [1]
A Rational Function Model (RFM) based geo-positioning method utilizing some already orientated image(s) as the reference instead of using ground control points (GCPs) is presented, which is the RFM extension of the physical model based geo-positioning method previously developed by the first author [9]
The mathematical details of the RFM based geo-positioning method were developed, in which the systematic errors are compensated by an affine transformation and the rank-deficiency of the coefficient matrix of the normal equation is solved using an iterative method for correcting characteristic values and introducing orientated images method
Summary
Earth observation satellites provide an invaluable means for mapping and monitoring the Earth’s environment through various spatial, spectral and temporal resolutions, such as mapping rivers and vegetation for environmental applications, creating feature and elevation maps for topography mapping, mapping coastlines for renewable natural resources applications, and so on [1] In these mapping cases, accurate exterior orientation parameters (EOPs) and the consistent inner precision of the images are essential, which are obtained by the block adjustment method. A mathematical model considering the thermo-elastic effects on the satellite was presented and applied to the UK-DMC images, and a geo-positioning accuracy of 0.5–1 km was achieved [3] In another approach, “virtual” control points generated using the auxiliary data and imaging model were taken as an alternative of GCPs to be put into the adjustment [4]. This paper presents the further development of this method to extend its application to the case where satellite imagery does not provide its physical model but a Rational Function Model (RFM) instead
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