RTC-GAN: REAL-TIME CLASSIFICATION OF SATELLITE IMAGERY USING DEEP GENERATIVE ADVERSARIAL NETWORKS WITH INFUSED SPECTRAL INFORMATION

This paper implements a deep learning-based Convolutional Neural Network (CNN) with adversarial training and infused pixel information to classify multi-spectral data into 4 LULC classes and cloud. The network is capable of classifying the image on a real-time basis at acquisition time in pixel level by considering the various spectral band values at the pixel and a spatial region around the pixel to collect the spatial features. This way, both spatial information, and spectral information are considered to classify the image. This novel GAN architecture named RTC-GAN is generalized over all the satellites that have their sensors in and around standard NIR, R and G spectral bands while being able to classify the images in realtime. This network is realized and tested on data obtained from satellites Landsat 8 Sentinel2 and Indian Remote Sensing (IRS) satellites like Cartosat-2S, Resourcesat-2/2A. The dataset is not open-sourced and hence very minimal information is provided regarding the IRS data.