With the rapid development of remote sensing satellites

the size and the resolution of satellite images is growing 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 enhancement method based on sparse recognition to increase the number of control points and to improve the matching precision for high-resolution images. First

feature vectors of images are constructed by computing the image radiation-parameters. Second

the classification of sub-region terrain in the image can be determined using sparse recognition. Finally

according to the specific type of the sub-region terrain

we enhance the regions by the Wallis filter adaptively 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 ZY-3 images

hence can increase the number of and improve accuracy of control points. In this paper

we propose a new enhancement algorithm for high-resolution remote sensing images that we enhance textures of sub-region terrains using adaptive Wallis filter. Compared with traditional Wallis filter

our method eliminates the number of pixels with saturated gray to increase the number of and improve accuracy of control points.