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Clouds and snow have similar spectral features in the visible andnear-infrared (VNIR) range and are thus difficult to distinguish from eachother in high resolution VNIR images. We address this issue by introducing ashortwave-infrared (SWIR) band where clouds are highly reflective, and snow isabsorptive. As SWIR is typically of a lower resolution compared to VNIR, thisstudy proposes a multiresolution fully convolutional neural network (FCN) thatcan effectively detect clouds and snow in VNIR images. We fuse themultiresolution bands within a deep FCN and perform semantic segmentation atthe higher, VNIR resolution. Such a fusion-based classifier, trained in anend-to-end manner, achieved 94.31% overall accuracy and an F1 score of 97.67%for clouds on Resourcesat-2 data captured over the state of Uttarakhand, India.These scores were found to be 30% higher than a Random Forest classifier, and10% higher than a standalone single-resolution FCN. Apart from being useful forcloud detection purposes, the study also highlights the potential ofconvolutional neural networks for multi-sensor fusion problems.

Multiresolution Fully Convolutional Networks to detect Clouds and Snow through Optical Satellite Images