Abstract
Abstract. Geometric accuracy of the remote sensing rectified image is usually evaluated by the root-mean-square errors (RMSEs) of the ground control points (GCPs) and check points (CPs). These discrete geometric accuracy index data represent only on a local quality of the image with statistical methods. In addition, the traditional methods only evaluate the difference between the rectified image and reference image, ignoring the degree of the original image distortion. A new method of geometric quality evaluation of remote sensing image based on the information entropy is proposed in this paper. The information entropy, the amount of information and the uncertainty interval of the image before and after rectification are deduced according to the information theory. Four kind of rectification model and seven situations of GCP distribution are applied on the remotely sensed imagery in the experiments. The effective factors of the geometrical accuracy are analysed and the geometric qualities of the image are evaluated in various situations. Results show that the proposed method can be used to evaluate the rectification model, the distribution model of GCPs and the uncertainty of the remotely sensed imagery, and is an effective and objective assessment method.
Highlights
Geometric rectification is an important part of remote sensing information processing, directly related to the accuracy and usefulness of the information
The accuracies of the corrected images are different based on different georeferencing control data, specially related to the number, distribution and accuracy of ground control points (GCPs), and DEM in different scale (Cressie, 1991, Jiao et al, 2008, Wang&Ge, 2011)
The information entropy, the amount of information and the uncertainty interval of the image before and after rectification are deduced according to the information theory
Summary
Geometric rectification is an important part of remote sensing information processing, directly related to the accuracy and usefulness of the information. It is the basis of remote sensing image processing and applications. The geometric accuracy is affected by the method of geometric model optimization and parameter solving (Long, et al, 2014a, Jiao, et al, 2013). Geometric rectification models of remote sensing images, such as the rigorous physical model, rational function model (RFM), polynomial model, etc., normally are very complicated. The factors that affect on the model’s accuracy are the selection of mathematical function, and the optimization of the parameters, which is
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