Continuous change detection and classification using hidden Markov model: A case study for monitoring urban encroachment onto farmland in Beijing

Yuan Yuan, Yu Meng, Lei Lin, Hichem Sahli, Anzhi Yue, Jingbo Chen, Zhongming Zhao, Yunlong Kong, Dongxu He

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and "from-to" information. This method is based on a hidden Markov model (HMM) trained for each land cover class. Assuming a pixel's initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient.

Original languageEnglish
Pages (from-to)15318-15339
Number of pages22
JournalRemote Sensing
Volume7
Issue number11
DOIs
StatePublished - 2015
Externally publishedYes

Keywords

  • Change detection
  • Classification
  • Hidden semi-Markov model (HSMM)
  • Satellite image time series
  • Urban encroachment onto farmland

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