Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 35, Issue 11-12, Pages 4009-4030Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2014.916054
Keywords
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Funding
- National Natural Science Foundation of China [61273317]
- National Top Youth Talents Programme of China
- Specialized Research Fund for the Doctoral Programme of Higher Education [20130203110011]
- Fundamental Research Fund for the Central Universities [K50510020001, K5051202053]
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The Yellow River Estuary area of China is under great pressure from both human intervention and natural processes. For analysis of the changes in this area, this article presents a novel change-detection method based on a local fit-search model and kernel-induced graph cuts in multitemporal synthetic aperture radar images. Change detection involves assigning a label to every pixel. This task is naturally formulated in terms of energy minimization, which can be effectively solved by graph cuts. The difference image is transformed implicitly by a kernel function so that an alternative to complex modelling of the original data makes the piecewise constant model become applicable for graph cuts formulation. An issue is that graph cuts are sensitive to the initial estimate. The local fit-search model is proposed to approximate to the local histogram while selecting an optimal threshold for the initial labelling, which leads to an effective constraint for graph cuts and computational benefits as well. Visual and quantitative analyses obtained on the Yellow River Estuary data set confirm the effectiveness of the proposed method and that it outperforms the other state-of-the-art methods of change detection.
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