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dc.contributor.authorYavariabdi, A.
dc.contributor.authorKusetogullari, H.
dc.contributor.authorMendi, E.
dc.contributor.authorKarabatak, B.
dc.date.accessioned2020-08-07T12:52:37Z
dc.date.available2020-08-07T12:52:37Z
dc.date.issued2018
dc.identifier10.1109/IS.2018.8710473
dc.identifier.issn9781538670972 (ISBN)
dc.identifier.urihttp://hdl.handle.net/20.500.12498/2864
dc.description.abstractIn this paper, a novel unsupervised change detection method is proposed to automatically detect changes between two cloud-contaminated Landsat images. To achieve this, firstly, a photometric invariants technique with Stationary Wavelet Transform (SWT) are applied to input images to decrease the influence of cloud and noise artifacts in the change detection process. Then, mean shift image filtering is employed on the sub-band difference images, generated via image differencing technique, to smooth the images. Next, multiple binary change detection masks are obtained by partitioning the pixels in each of the smoothed sub-band difference images into two clusters using Fuzzy c-means (FCM). Finally, the binary masks are fused using Markov Random Field (MRF) to generate the final solution. Experiments on both semi-simulated and real data sets show the effectiveness and robustness of the proposed change detection method in noisy and cloud-contaminated Landsat images. © 2018 IEEE.
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.source9th International Conference on Intelligent Systems, IS 2018
dc.titleUnsupervised Change Detection using Thin Cloud-Contaminated Landsat Images
dc.typeConference Paper


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