目的 随着国内遥感卫星的迅速发展，卫星图像的图幅越来越大，分辨率越来越高。在轨遥感图像的几何精度评价，要求从待评遥感图像和多源参考图像之间精确地提取出分布均匀的控制点信息。使用Wallis滤波对高分辨率影像进行增强时，会产生过增强和饱和现象，影响了控制点提取效果。为了克服上述缺陷，提出了一种基于稀疏识别的自适应Wallis图像增强算法。方法 首先计算图像子区域的辐射质量参数并构建分类特征；然后通过稀疏识别算法确定子区域的地物类型；最后根据子区域所属地物类型，选择不同的Wallis滤波参数，实现整幅图像的自适应增强，并在增强的遥感图像上提取控制点信息，实现遥感图像的几何精度自动化评价。结果 针对资源三号卫星影像的实验结果表明，针对不同的子区域地物类型进行自适应Wallis增强，有效防止了基于全局统一参数的Wallis滤波带来的过增强和饱和现象，有效增强了高分辨率图像的纹理。结论 提出了一种新的高分辨率遥感影像增强策略，增强了高分辨率图像的纹理，提高了控制点的获取数目和准确率。

Objective 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. Method 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. Result 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. Conclusion 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.