IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Prediction Changes for Nonstationary Multi-Temporal Satellite Images Using HMM

Prediction Changes for Nonstationary Multi-Temporal Satellite Images Using HMM
View Sample PDF
Author(s): Ali Ben Abbes (ENSI, Tunisia) and Imed Riadh Farah (ISAMM, Tunisia)
Copyright: 2019
Pages: 20
Source title: Environmental Information Systems: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-7033-2.ch053

Purchase

View Prediction Changes for Nonstationary Multi-Temporal Satellite Images Using HMM on the publisher's website for pricing and purchasing information.

Abstract

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).

Related Content

Seda Yıldırım, Durmuş Çağrı Yıldırım. © 2020. 22 pages.
José G. Vargas-Hernández. © 2020. 26 pages.
Kappina Kasturige Kamani Sylva. © 2020. 22 pages.
Giovanni Patriarca, Diana M. Valentini. © 2020. 15 pages.
Rui Zhao, Yuxin Huang, Yuyu Zhou, Meng Yang, Xinyue Liu. © 2020. 18 pages.
Huynh Viet Khai. © 2020. 15 pages.
Sebastiano Patti, Antonino Messina. © 2020. 20 pages.
Body Bottom