I had worked on this project under the guidance of Chintan Maniyar (a Researcher at ISRO) on removing the clouds from satellite images using Generative Adversarial Networks.
Cloud cover in the earth's atmosphere is a major issue in temporal optical satellite image processing. Clouds, thick or thin, cover the earth features in a satellite image and hide important information. The objective of this project is to develop a deep learning based automated pipeline to remove cloud cover from optical satellite imagery. Conditional GANs architecture is used to learn the mapping of the cloudy satellite image to it's cloud-free counterpart. A novel augmented and computationally efficient training approach is suggested.
We had used pix-2-pix GANS for the same. pix-2-pix GAN model is used for learning the mapping of the cloudy image to it's cloud-free counterpart. It follows a supervised conditional vector based training approach. The model is trained on the cloudy and cloud-free image pairs which are dated two days apart. pix-2-pix model follows a pixel to pixel image restoration approach and generates a new pixel cloud-free for every cloudy pixel.