Journal
REMOTE SENSING
Volume 7, Issue 11, Pages 15318-15339Publisher
MDPI
DOI: 10.3390/rs71115318
Keywords
classification; change detection; hidden semi-Markov model (HSMM); satellite image time series; urban encroachment onto farmland
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Funding
- State Key Laboratory of Remote Sensing Science in China [OFSLRSS201406]
- National Natural Science Foundation of China [41301393, 41401474]
- National Science and Technology Major Project [30-Y20A03-9030-15/16]
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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.
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