Abstract
Thin clouds in remote sensing images increase the radiometric distortion of land surfaces. The identification of pixels contaminated by thin clouds, known as the thin-cloud mask, is an important preprocessing procedure to guarantee the proper utilization of data. However, failure to effectively separate thin clouds and high-reflective land-cover features causes thin-cloud masks to remain a challenge. To overcome this problem, we developed a thin-cloud masking method for remote sensing images based on sparse dark pixel region detection. As a result of the effect of scattering, the path radiance is added to the radiance recorded by the sensor in the thin-cloud area, which causes the number of dark pixels in the thin-cloud area to be much less than that in the clear area. In this study, the area of a Thiessen polygon (a nonparametric measure) is used to evaluate the density of local dark pixels, and the region with the sparse dark pixel is selected as the thin-cloud candidate. Then, thin-cloud and clear areas are used as samples to train the background suppression haze thickness index (BSHTI) transform parameters, and convert the original multiband images into single-band images. Finally, an accurate thin-cloud mask is obtained for every buffered thin-cloud candidate, via the segmentation of the BSHTI band. Additionally, the multispectral images obtained by the Wide Field View (WFV), on board the Chinese GaoFen1, and the Operational Land Imager (OLI), on board the Landsat 8, are employed to evaluate the performance of the method. The results reveal that the proposed approach can obtain a thin-cloud mask with a high true-value ratio and detection ratio. Thin-cloud masks can satisfy various application demands.
Highlights
Thin-cloud areas in remote sensing images exhibit information regarding both thin clouds and the underlying land surface; the radiometric distortion of the land cover will result in image classification and target detection errors, which restricts land cover-based applications
By carefully observing the boundaries between thin clouds and land surface features, we found that the extracted edge adhered to the true edge between the clouds and the clear part of the image
It should be noted that sea surfaces or broad water surfaces, which had large amounts of low reflectance dark pixels, combined with an undetermined threshold, detected a certain amount of false dark pixels, which led to the missing detection of thin clouds
Summary
Thin-cloud areas in remote sensing images exhibit information regarding both thin clouds and the underlying land surface; the radiometric distortion of the land cover will result in image classification and target detection errors, which restricts land cover-based applications. The thin-cloud mask, which aims to delimit pixels contaminated by thin clouds, is a critical preprocessing step to ensure the accurate utilization of data. Influenced by the thin-cloud thickness, diversity and complexity of obscured land covers, it is less likely to describe thin clouds via a uniform spectral characteristic. It is difficult to utilize the traditional thick-cloud detection methods for thin-cloud masks. To overcome the aforementioned problems, several thin-cloud detection/mask approaches have been successfully developed according to the characteristics of thin clouds. Similar to those in Sun et al [1], we can classify these approaches into three main categories: the transform method, decomposition method, and dark object method
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