SNOW AND CLOUD DISCRIMINATION USING CONVOLUTIONAL NEURAL NETWORKS
- 1Indian Institute of Remote Sensing, Dehradun, Uttarakhand, India
- 2Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Keywords: Convolutional Neural Networks, SWIR, ReLU, Machine Learning, Remote Sensing
Abstract. Snow is an important feature on our planet, and measuring its extent has advantages in climate studies. Snow mapping through satellite remote sensing is often affected by cloud cover. This issue can be resolved by using short wave infrared (SWIR) sensors. In order to obtain an effective cloud mask, our study aims to use SWIR data of a ResourceSat-2 satellite. We employ Convolutional Neural Networks (CNN) to discriminate similar pixels of clouds and snow. The technique is expected to give a high accuracy compared to traditional methods such as thresholding. The cloud mask thus produced can also be used for creating the metadata for Indian Remote Sensing products.