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Unsupervised Change Detection using Thin Cloud-Contaminated Landsat Images
Karatay Üniversitesi, TUR.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0001-7536-3349
Karatay Üniversitesi, TUR.
Turkcell, Nicosia, CYP.
2018 (English)In: 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings / [ed] JardimGoncalves, R; Mendonca, JP; Jotsov, V; Marques, M; Martins, J; Bierwolf, R, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 21-25Conference paper, Published paper (Refereed)
Abstract [en]

In 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 21-25
Keywords [en]
Change detection, Fuzzy c-means, Landsat images, Mean-shift, Wavelet, Fuzzy systems, Image segmentation, Intelligent systems, Magnetorheological fluids, Markov processes, Fuzzy C mean, Mean shift, Wavelet transforms
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:bth-18024DOI: 10.1109/IS.2018.8710473ISI: 000469337900004Scopus ID: 2-s2.0-85065972639ISBN: 9781538670972 (print)OAI: oai:DiVA.org:bth-18024DiVA, id: diva2:1324914
Conference
9th International Conference on Intelligent Systems, IS 2018; Funchal - Madeira; Portugal; 25 September 2018 through 27 September
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-07-01Bibliographically approved

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Kusetogullari, Hüseyin

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