Abstract
Traditional Markov random Field (MRF) methods assume that neighboring pixels tend to have the same label. However, this assumption is always inconsistent with the actual situation and affects the resultant accuracy of the algorithm. To overcome this, we propose an object-based Markov Random Field (OMRF) model and a change detection method based on OMRF model. The OMRF model assumes that pixels within same object are in the same class. First, we generate the difference image from multi-temporal remote sensing images. Second, Mean Shift is applied to extract objects from difference image. Finally, change detection map is generated by iterative algorithm. The experimental results show that the algorithm can effectively improve the detection accuracy of the algorithm on real remote sensing datasets.
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© 2013 Springer-Verlag Berlin Heidelberg
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Liao, F., Yu, S., Li, Y., Zhang, Y. (2013). An Improved Method in Change Detection of Multitemporal Remote Sensing Image. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_74
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DOI: https://doi.org/10.1007/978-3-642-42057-3_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
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