Adaptive Wallis Filter via Sparse Recognition for Automatic Control Points Extraction

With the rapid development of the remote sensing satellite, the size and the resolution of satellite images grow increasingly. The evaluation of remote sensing image quality requires precise information of control points extracted from unevaluated images and reference images. Therefore, we propose an adaptive Wallis filter method based on sparse recognition to increase the number of control points and improve the matching precision. Firstly, feature vectors of images are constructed by computing the image radiation-parameters. Secondly, the classification of sub-region terrain in the image can be determined using sparse recognition. Finally, according to specific type of sub-region terrain, we enhance the regions by the Wallis filter based on corresponding filter parameters and extract control points which would lead to the automatic evaluation for geometric precision. The experiments show that the proposed method can get better results especially in the detail on the images of Resourse-3 satellite, hence can increase the number and improve accuracy of control points.