【摘要】 为解决现行云地检测算法不适用于亚米级全色遥感影像云地检测的问题,提出一种大尺度自适应匹配阈值(LS-AMTH,large-scale adaptive matching threshold)算法。算法构建包含光谱、纹理与边缘特征的特征参量集,利用提升算法对影像子块进行大尺度云地分类;之后对大尺度分类所得云地子块进行阈值的自适应匹配选择,最终实现像素级云地区域检测并统计云地占比。试验表明,针对亚米级全色影像,本文算法准确度达97.3%,在复杂云地混合区域取得良好检测效果。更多还原

【Abstract】 To solve the problem of the inapplicability of the existing cloud detection algorithm in the cloud detection of panchromatic remote sensing images at sub-meter level,a large scale adaptive matching threshold(LS-AMTH)algorithm is proposed in this paper.In the algorithm,the feature set composed of spectrum,texture and edge is structured,and the sub-blocks of images in large scale are classified by using the boost algorithm first.We can obtain a series of cloud region images(some images may include less ground regions)and ground region images(some images may include less cloud regions)through this step.In order to detect the ground region pixels in cloud region images and cloud region pixels in ground region images,the classified sub-blocks are selected by adaptive threshold matching respectively next.The cloud detection in pixel-scale is obtained and the ratio of cloud is computed at last.The results show that the algorithm has better performance than traditional algorithms,the accuracy of this algorithm is no less than 97.3% with regard to panchromatic remote sensing images at sub-meter level and the detection results in complicated cloud-ground mixed regions are fine.更多还原