Automatic Cloud Detection in Remote Sensing Imagery Using Saliency-Based Mixed Features

Cloud detection plays an important role in remote sensing image quality evaluation, information acquisition, and analysis. For most optical satellites, the acquired multispectral bands include only three visible bands and one near-infrared band. How to achieve fast and accurate cloud detection with this limited number of bands is a problem worth studying. In this paper, a cloud detection algorithm (SMFCD) based on a saliency-based mixed feature map of images is proposed. This algorithm first calculates the saliency characteristics from the band mean and haze optimized transformation (HOT) map. The transmission map obtained from dark channel prior theory is used to combine the mixed feature map for subsequent detection. The proposed algorithm generates the initial thick cloud mask using a segmentation method based on the Otsu algorithm later. Guided filtering is then used to refine the cloud mask. The final cloud detection result is acquired through post processing. The algorithm is tested on six different datasets. It is shown that the algorithm can obtain good detection results for most images, with overall accuracies from 0.860 to 0.969 on these datasets. The average time consumption on these datasets reaches 4.4~42.1 seconds, and a fast version of the proposed algorithm reduces these times by about 50%. The proposed algorithm can be used for remote sensing image quality evaluation and subsequent application preprocessing with greater adaptability and flexibility than existing algorithms, since it does not depend on specific satellite radiometric calibration coefficients.