Volume IV-5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 59-63, 2018
https://doi.org/10.5194/isprs-annals-IV-5-59-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 59-63, 2018
https://doi.org/10.5194/isprs-annals-IV-5-59-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Nov 2018

15 Nov 2018

SNOW AND CLOUD DISCRIMINATION USING CONVOLUTIONAL NEURAL NETWORKS

D. Varshney1,2, P. K. Gupta1, C. Persello2, and B. R. Nikam1 D. Varshney et al.
  • 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.