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An Object-Oriented Binary Change Detection Method Using Nearest Neighbor Classification

Abstract : Threshold selection is a critical step in using binary change detection methods. The threshold determines the accuracy of change detection results but is highly subjective and scene-dependent, depending on the familiarity with the study area and the analyst’s skill. Nearest neighbor classification is a non-parametric classifier, which was applied to remove the threshold. In order to find the most suitable feature to detect construction and farmland changes, a variety of single and multiple variables were explored. They were regional similarity (RSIM), brightness difference images (BDIs), multi-band difference images (MDIs), multi-band ratio difference images (MRDIs), a combination of RSIM and BDIs (RSIMBD), a combination of RSIM and a optimum band difference and a optimum band ratio difference (RSIMDR), MDIs and MRDIs multiple variable groups. All were tested for two study sites of the bi-temporal SPOT 5 imagery, the results indicated that RSIM, RSIMDR, RSIMBD were significantly better than other single and multiple variables.
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Liang Jie, Jianyu Yang, Chao Zhang, Jiabo Sun, Dehai Zhu, et al.. An Object-Oriented Binary Change Detection Method Using Nearest Neighbor Classification. 6th Computer and Computing Technologies in Agriculture (CCTA), Oct 2012, Zhangjiajie, China. pp.394-406, ⟨10.1007/978-3-642-36137-1_46⟩. ⟨hal-01348256⟩

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