Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [17 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, July 22, 2016 - 3:56:33 PM
Last modification on : Tuesday, October 23, 2018 - 5:22:01 PM
Long-term archiving on: : Sunday, October 23, 2016 - 1:26:56 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



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⟩



Record views


Files downloads