高分辨率遥感影像中云和似云目标的自动区分

云的存在会对遥感影像的处理及目标识别等产生影响,因此,自动提取云对高分辨率卫星影像的应用具有重要意义。高分影像上更加复杂的云的细节形态及似云目标的干扰,使得高分影像的自动云提取难以达到实用水平。本文以雪地为例,选取形状、纹理和边缘3个差异化特征作为云与似云目标区分的关键,提出了一种区分高分辨率遥感影像中云和似云目标的云检测算法。首先利用Wallis滤波对输入影像进行预处理,增强影像中不同尺度的影像纹理模式;然后对影像进行快速稳定的均值漂移分割,利用灰度和纹理特征构成支持向量机的第一层分类器,将分割后的区域对象分成"云"和普通地物,再利用边缘、形状、纹理等特征结合灰度特征构成支持向量机的第二层分类器,将"云"区分为云区和似云目标;最后使用Grab-cut对云检测结果进行边缘迭代精化。本文算法取得了优良的试验结果,证明了算法在似云目标干扰下对高分辨率遥感影像进行精确云检测的能力。

Automatic Discrimination of Cloud and Cloud-like Target in High Resolution Satellite Imagery

Clouds in remote sensing imagery have an impact on its process and subsequent target recognition.Thus, automatic cloud extraction is essential to the application of high-resolution imagery.The complex shapes of the clouds in high-resolution imagery and the interference of cloud-like targets make it difficult to achieve a practical automatic cloud extraction.In this paper, we choose snow as the example of cloud-like target, and develop an algorithm which chooses shape, texture and edge as the key features to discriminate cloud from cloud-like targets.Firstly, the input image is preprocessed with Wallis filtering to enhance texture patterns at different scales.Then the input is segmented by a fast stable mean-shift segmentation.The first support vector machine classifier is built with gray and texture features, which divides all segmented parts into “cloud” and common ground targets.A second classifier is built with edge, shape and texture features to divide “cloud” areas into clouds and cloud-like targets.Finally, Grab-cut is applied to refine edges of cloud extraction results iteratively.Experiments achieve good results and demonstrate the algorithm’s capability to extract clouds in high-resolution imagery precisely with the interference of cloud-like targets.