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Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM

Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM

Ali Ben Abbes, Imed Riadh Farah
Copyright: © 2017 |Pages: 20
ISBN13: 9781522509370|ISBN10: 1522509372|EISBN13: 9781522509387
DOI: 10.4018/978-1-5225-0937-0.ch015
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MLA

Ben Abbes, Ali, and Imed Riadh Farah. "Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM." Handbook of Research on Geographic Information Systems Applications and Advancements, edited by Sami Faiz and Khaoula Mahmoudi, IGI Global, 2017, pp. 387-406. https://doi.org/10.4018/978-1-5225-0937-0.ch015

APA

Ben Abbes, A. & Farah, I. R. (2017). Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM. In S. Faiz & K. Mahmoudi (Eds.), Handbook of Research on Geographic Information Systems Applications and Advancements (pp. 387-406). IGI Global. https://doi.org/10.4018/978-1-5225-0937-0.ch015

Chicago

Ben Abbes, Ali, and Imed Riadh Farah. "Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM." In Handbook of Research on Geographic Information Systems Applications and Advancements, edited by Sami Faiz and Khaoula Mahmoudi, 387-406. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0937-0.ch015

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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).

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