变序字典学习AO-DL的资源三号遥感影像云去除

摘要

In this paper,a new cloud removal method in remote sensing images is adopted.Based on the theory of compressive sensing,this method combines K-SVD dictionary learning with the orthogonal matching pursuit(OMP)algorithm of sparse representation.At the same time,a specific sorting rule is added to the process of dictionary atoms training,so that each image dictionary has its own image properties while its atoms also have a similar arrangement order to reduce the interference between image differences.In this method,the good effect of reconstruction of the contaminated region by clouds and shadows in remote sensing images is achieved.To illustrate the performance of the proposed method, experiments on two sets of data of multitemporalZY-3 images at the same area are discussed.%采用了一种压缩感知方法进行遥感影像去云.该方法以压缩感知为理论基础,在采用K-SVD字典学习与稀疏表示的正交匹配追踪算法(OMP)相结合的同时,在字典原子训练的过程中加入某种特定的排序规则,使得各个影像字典在拥有各自影像属性的同时其原子也具备相似的排列顺序,减小影像间差异的干扰,使得遥感影像受云和阴影污染区域的重建取得良好的效果.最后应用两组相同地区不同时域的资源三号卫星影像进行了试验验证.