Optimizing WorldView-2,-3 cloud masking using machine learning approaches

The detection of clouds is one of the first steps in the pre-processing of remotely sensed data. At coarse spatial resolution (> 100 m), clouds are bright and generally distinguishable from other landscape surfaces. At very high-resolution (< 3 m), detecting clouds becomes a significant challenge due to the presence of smaller features, with spectral characteristics similar to other land cover types, and thin (partially transparent) cloud forms. Furthermore, at this resolution, clouds can cover many thousands of pixels, making both the center and boundaries of the clouds prone to pixel contamination and variations in the spectral intensity. Techniques that rely solely on the spectral information of clouds underperform in these situations. In this study, we propose a multi-regional and multi-sensor deep learning approach for the detection of clouds in very high-resolution WorldView satellite imagery. A modified UNet-like convolutional neural network (CNN) was used for the task of semantic segmentation in the regions of Vietnam, Senegal, and Ethiopia strictly using RGB + NIR spectral bands. In addition, we demonstrate the superiority of CNNs cloud predicted mapping accuracy of 81-91%, over traditional methods such as Random Forest algorithms of 57-88%. The best performing UNet model has an overall accuracy of 95% in all regions, while the Random Forest has an overall accuracy of 89%. We conclude with promising future research directions of the proposed methods for a global cloud cover implementation.

Automated summary

In this study, a deep learning approach based on convolutional neural networks (CNNs) is proposed for cloud detection in very high-resolution satellite imagery. The CNNs exhibit superior mapping accuracy compared to traditional methods, with the best performing model achieving an overall accuracy of 95%.