A New Method for Geometric Quality Evaluation of Remote Sensing Image Based on Information Entropy

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.

Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS

“…Information entropy [ 52 ] is the measurement of the expected value of information and represents the uncertainty of an event, which has been used in many areas, such as remote sensing image evaluation [ 53 ], classification [ 54 ] and image matching [ 55 ]. This section exploits information entropy to measure the distribution coherence of point clouds from two neighboring scans.…”