基于多特征融合的层次支持向量机遥感图像云检测

云检测是遥感图像处理和应用的前提,针对遥感图像云检测的准确率容易受到薄云及似云地物影响的挑战,提出一种结合遥感影像灰度、纹理和频率特征的层次支持向量机云检测算法.该方法首先采用简单线性迭代聚类算法将遥感图像分割为像素块,再采用一种层次支持向量机分类器对遥感图像以像素块为单位进行云检测.层次支持向量机的第一层将像素块初步...

Cloud Detection of Remote Sensing Images Based on H-SVM with Multi-Feature Fusion

Cloud detection is the prerequisite of remote sensing image processing and application. It is a widelychallenge that the accuracy of cloud detection from remote sensing image is easily influenced by thin clouds andcloud-like ground targets. Therefore, a hierarchical support vector machine (H-SVM) cloud detection algorithmcombining grayscale, texture, and frequency features of remote sensing image is proposed in this work. Firstly, asimple linear iterative clustering algorithm is used to segment the remote sensing image into pixel blocks. Secondly,a H-SVM classifier is designed to perform cloud detection on the segmented pixel blocks, where the first layer ofthe H-SVM preliminarily divides the pixel blocks into “cloud” and “landscape categories”, and the second layercontaining two classifiers further classifies the classification results of the first layer and then merges the classifiedresults into three categories of “thick cloud”, “thin cloud”, and “land features”. Finally, the classification resultsare processed using expansion algorithm to get the final cloud detection results. RGB band remote sensing imagesof GF-1 WFV are selected for verification experiments. It is shown that the method proposed in this study has anaverage accuracy of 95.4% for the cloud detection in the experimental images, which indicates that the method canbe used for cloud detection of remote sensing images in multiple scenarios, and serve the production and applicationof remote sensing products.