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Detecting and masking cloud and cloud shadow from satellite remote sensingimages is a pervasive problem in the remote sensing community. Accurate andefficient detection of cloud and cloud shadow is an essential step to harnessthe value of remotely sensed data for almost all downstream analysis. DeepMask,a new algorithm for cloud and cloud shadow detection in optical satelliteremote sensing imagery, is proposed in this study. DeepMask utilizes ResNet, adeep convolutional neural network, for pixel-level cloud mask generation. Thealgorithm is trained and evaluated on the Landsat 8 Cloud Cover AssessmentValidation Dataset distributed across 8 different land types. Compared withCFMask, the most widely used cloud detection algorithm, land-type-specificDeepMask models achieve higher accuracy across all land types. The averageaccuracy is 93.56%, compared with 85.36% from CFMask. DeepMask also achieves91.02% accuracy on all-land-type dataset. Compared with other CNN-based cloudmask algorithms, DeepMask benefits from the parsimonious architecture and theresidual connection of ResNet. It is compatible with input of any size andshape. DeepMask still maintains high performance when using only red, green,blue, and NIR bands, indicating its potential to be applied to other satelliteplatforms that only have limited optical bands.

DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network