Multiresolution Fully Convolutional Networks to detect Clouds and Snow through Optical Satellite Images
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Clouds and snow have similar spectral features in the visible and near-infrared (VNIR) range and are thus difficult to distinguish from each other in high resolution VNIR images. We address this issue by introducing a shortwave-infrared (SWIR) band where clouds are highly reflective, and snow is absorptive. As SWIR is typically of a lower resolution compared to VNIR, this study proposes a multiresolution fully convolutional neural network (FCN) that can effectively detect clouds and snow in VNIR images. We fuse the multiresolution bands within a deep FCN and perform semantic segmentation at the higher, VNIR resolution. Such a fusion-based classifier, trained in an end-to-end manner, achieved 94.31 for clouds on Resourcesat-2 data captured over the state of Uttarakhand, India. These scores were found to be 30 10 cloud detection purposes, the study also highlights the potential of convolutional neural networks for multi-sensor fusion problems.
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