A robust ragged cloud detection algorithm for remote sensing image

Abstract

Cloud detection plays a significant role in remote sensing (RS) image processing. Numbers of cloud detection algorithms have been developed in the literature. However, they suffer the weakness of omitting thin and small cloud, and poor ability of differentiating the cloud from confusing ground region (e.g. artificial building). In this study, a robust ragged cloud detection algorithm for RS image is proposed. First, the simple linear iterative clustering method is applied to segment ragged cloud. Then, the improved Qtsu's method is introduced to remove the redundant superpixel. Finally, the Natural Scene Statistic is designed to classify the cloud region. Finally, original image will be classified into thick cloud, thin cloud and non-cloud. Experimental results indicate that the proposed model outperforms the state-of-the-art methods for cloud detection.

Keywords

• natural scenes
• image segmentation
• geophysical image processing
• remote sensing
• image classification
• clouds
• atmospheric techniques
• cloud detection algorithms
• rs image
• segment ragged cloud
• cloud region
• remote sensing image processing
• qtsu method
• natural scene statistic