Affiliation:
1. Manipal Institute of Technology, India & Manipal Academy of Higher Education, India
2. Vellore Institute of Technology, Chennai, India
Abstract
Satellite images of the entire globe or any given location can be procured quickly with good resolution from any part of the world using thousands of artificial satellites revolving around the Earth. Using advanced AI/ML image processing algorithms, the acquired data can be analyzed to obtain various essential knowledge of any place at any time like the chemical composition of the environment, population density (social), etc. Another prominent field is military, defence, and warfare. Independent hyperspectral image cluster analysis of the world's heavily populated cities like Delhi, Shanghai, etc. has clearly shown the migration of population from rural to the urban. The RS-GIS technology combined with advanced machine learning algorithms predicts that only 50 more years of groundwater supply is left to be harvested. These case observations from different parts of the world show the power and scope of aero-oriented image processing using machine learning algorithms.
Reference14 articles.
1. Disaster detection from aerial imagery with convolutional neural network
2. Cao, Q. D., & Choe, Y. (2018), Deep Learning Based Damage Detection on Post Hurricane Satellite Imagery. arXiv preprint arXiv:1807.01688.
3. Demir, Koperski, Lindenbaum, Pang, Huang, Basu, Hughes, Tuia, & Raskar. (2018). Deepglobe: A challenge to parse the earth through satellite images. ArXiv e-prints.
4. Residual Inception Skip Network for Binary Segmentation
5. SATELLITE IMAGE CLASSIFICATION OF BUILDING DAMAGES USING AIRBORNE AND SATELLITE IMAGE SAMPLES IN A DEEP LEARNING APPROACH