Abstract
Due to their level of spatial detail (pixel dimensions equal to or less than 1 m), very high-resolution satellite images (VHRSIs) need particular georeferencing and geometric corrections which require careful orthorectification. Although there are several dedicated algorithms, mainly commercial and free software for geographic information system (GIS) and remote sensing applications, the quality of the results may be inadequate in terms of the representation scale for which these images are intended. This paper compares the most common orthorectification algorithms in order to define the best approach for VHRSIs. Both empirical models (such as 2D polynomial functions, PFs; or 3D rational polynomial functions, RPFs) and rigorous physical and deterministic models (such as Toutin) are considered. Ground control points (GCPs) and check points (CPs)—whose positions in the image as, well as in the real world, are known—support algorithm applications. Tests were executed on a WorldView-2 (WV-2) panchromatic image of an area near the Gulf of Naples in Campania (Italy) to establish the best-performing algorithm. Combining 3D RPFs with 2D PFs produced the best results.
Highlights
Very high-resolution satellite images (VHRSIs) are widely used in several application fields, at both scientific and commercial levels, because of their detailed information.The geometric use of these images requires careful orientation and orthorectification in order to georeference the images and correct the geometric deformations introduced during acquisition [1].Remotely sensed images contain such significant geometric distortions that they cannot be used directly with map-based products in a geographic information system (GIS) [2]
Well-corrected and georeferenced VHRSIs such as IKONOS, WorldView-2 (WV-2), Quickbird, GeoEye, and Komposat are used for many purposes, i.e., map creation and updating, emergency mapping [3,4,5,6], vegetation mapping [7], coastline identification [8,9], etc
This paper describes orthorectification that was conducted on WorldView-2 (WV-2) imagery using several algorithms which are available in common commercial and free software
Summary
Very high-resolution satellite images (VHRSIs) are widely used in several application fields, at both scientific and commercial levels, because of their detailed information (pixel dimensions equal to or less than 1 m in panchromatic band). Sensed images contain such significant geometric distortions that they cannot be used directly with map-based products in a geographic information system (GIS) [2]. This characteristic is pertinent to VHRSIs for several reasons (e.g., the reduced dimensions of the pixels and the off-nadir viewing). Jawak et al [13] demonstrate how WV-2 imagery, compared to previous sensors, improves the identification of land-cover targets, and improves the land-cover classification
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