Deep Convolutional Networks for Cloud Detection Using Resourcesat-2 Data

Cloud cover creates obstruction in Earth Observation studies. The obstruction is harder to distinguish from features having similar reflectance on the ground, such as snow. To distinguish clouds from snow in a VNIR image, we use an additional SWIR band. The images were fed into a deep Fully Convolutional Network that can fuse the multiresolution SWIR and VNIR bands together, in order to produce pixelwise classification. The accuracy obtained by the model on the test image was 93.35%. We compare the performance of this model with a more commonly used technique, Random Forests. To analyze the effect of SWIR, we use another deep learning model, trained only on the VNIR image, and compare the accuracies obtained.