Change detection and classification of satellite images using convolutional neural network
subject: Artificial intelligence; Computer vision; Convolutional neural network; Machine learning; Satellite image
description: Satellite and airborne imagery, collectively known as earth observation imagery, are images of the earth collected from spaceborne or airborne platforms such as satellites and aircraft. Over the last 100 years, with the fast development of aviation, space exploration, and imaging technologies, the coming together of these technologies has been inevitable. Earth observation imagery has many applications in regional planning, geology, reconnaissance, fishing, meteorology, oceanography, agriculture, biodiversity conservation, forestry, landscape, intelligence, cartography, education, and warfare. With the rise in the number of these airborne and spaceborne imaging platforms being deployed by government and private entities alike, the capability to sift through and analyze vast amounts of data generated by these platforms is the need of the hour. With the exponential improvement in the computational capabilities of computers over the last half a century, analysts are exceedingly moving towards the practice of artificial intelligence, machine learning (ML), and computer vision solutions to automate a large part of the processes employed in analyzing earth observation imagery. This work recommends a workflow to perceive and classify changes in earth observation imagery of a given area by utilizing the vast flexibility that convolutional neural networks (CNN) provide. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/.
creator: Srinivasiah, Raghavendra; Jankatti, Santosh Kumar; Lamani, Manjunath Ramanna; Jinachandra, Niranjana Shravanabelagola
source: IAES International Journal of Artificial Intelligence;Volume;15;Issue;1;pp.329-337
publisher: Institute of Advanced Engineering and Science
date: 01-01-2026
rights: All Open Access; Gold Open Access
relation: ISSN: 20894872;
format: online
language: English
type: Article
coverage: Srinivasiah R., Machine Learning and Data Science, School of Engineering and Technology, CHRIST University, Bangalore, India; Jankatti S.K., Department of Computer Science and Technology, School of Engineering, Dayananda Sagar University, Bangalore, India; Lamani M.R., Department of Computer Science and Engineering, Moodlakatte Institute of Technology, Kundapura, India; Jinachandra N.S., Department of Mechanical Engineering, School of Engineering and Technology, CHRIST University, Bangalore, India