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A Markov Random Field Approach to Improving Classification of Remotely Sensed Imagery by Incorporating Spatial and Temporal Contexts

Xu, Min
http://orcid.org/0000-0003-2066-0498
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439306355

Abstract Details

2015, MA, University of Cincinnati, Arts and Sciences: Geography.
This research uses Markov random fields to model stochastic interactions among classes over space and time, which allows a global Bayesian optimization of the classification result. A maximum likelihood supervised classification method is used to derive initial classification result purely based on the spectral information. Both the spatial and temporal contextual information are derived and represented from modeling the image pixel class labels in predefined spatial and temporal neighborhoods. In this study, we improve the spatial context by introducing the concept of distance decay and dealing with missing data by making the spatial context locally adaptive. For modeling the temporal context, we explore and evaluate a new method called “cubic spatio-temporal neighbor” besides commonly adopted transition probability matrices (TPMs) by considering the temporal correlation length and phonological pattern. The optimization of MRF model parameters (weights) for combining spectral, spatial and temporal information in the classification is an important problem. The possible occurrence of strongly undesirable negative parameters which are neglected in the literature is dealt with in this paper by using the scaled difference in coefficient estimates (DFBETAS) which is normally used in the field of Statistics. We have implemented the method using C# programming language and successfully applied it to the urban land cover classification with Landsat image time series over a decade and to the snow surface melting detection in Antarctic ice sheet with daily satellite observations. Our application examples demonstrate that the proposed algorithms are effective and efficient in modeling contextual information and optimizing parameters, and the incorporation of spatio-temporal information with our MRFs method results in significant improvement as large as 8.9% in classification in comparison with the use of spectral information alone.
Hongxing Liu, Ph.D. (Committee Chair)
Emily Kang, Ph.D. (Committee Member)
Michael Widener, Ph.D. (Committee Member)
46 p.
Geography
Markov random field; image classification; spatial context; temporal context; parameter optimization

Recommended Citations

Citations

  • Xu, M. (2015). A Markov Random Field Approach to Improving Classification of Remotely Sensed Imagery by Incorporating Spatial and Temporal Contexts [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439306355

    APA Style (7th edition)

  • Xu, Min. A Markov Random Field Approach to Improving Classification of Remotely Sensed Imagery by Incorporating Spatial and Temporal Contexts. 2015. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439306355.

    MLA Style (8th edition)

  • Xu, Min. "A Markov Random Field Approach to Improving Classification of Remotely Sensed Imagery by Incorporating Spatial and Temporal Contexts." Master's thesis, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439306355

    Chicago Manual of Style (17th edition)

ucin1439306355
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© 2015, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.