Abstract
Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification.
Highlights
Developments in satellite and space technologies have evolved rapidly, and new satellites with high-resolution sensors have steadily been launched to provide a variety of geospatial information for disciplines ranging from engineering to defense
The main aim of this study was to evaluate the impacts of the incidence angle, topography, land cover and spectral characteristics on the automatic production of Ground Control Points (GCPs) using Scale Invariant Feature Transform (SIFT) to perform Rational Polynomial Coefficients (RPC) refinement, determine how these parameters affect the results in terms of location accuracy after orthorectification, and evaluate how much the designed multi-thread approach accelerates the geometric correction process
This study proposed a fully automated process chain to improve the location accuracy of orthorectification with the use of GCPs produced by SIFT-based image matching
Summary
Developments in satellite and space technologies have evolved rapidly, and new satellites with high-resolution sensors have steadily been launched to provide a variety of geospatial information for disciplines ranging from engineering to defense. These developments enable users to adopt very high-resolution satellite images for large-scale applications, such as mapping urban areas, transportation network development, the identification of parcel-based agricultural boundaries for precision agriculture and the production of reliable geospatial information for homeland security [1,2,3,4]. The RPC model defines the relationship between the satellite image and the Earth’s surface as well as physical models with rational polynomials [5,9,11]
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