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Changes Monitoring in multitemporal satellite images using Iteratively Reweighted multivariate alteration detection (IR-MAD) algorithm and support vector machine (SVM) classification
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Armin Moghimi *   , Hamid Ebadi , Vahid Sadeghi |
| K.N.Toosi University of Technology |
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Abstract: (8747 Views) |
| Monitoring Land use changes is one of the important applications of remote sensing and geographic information system. In this study, a framework for change monitoring in multitemporal satellite images is presented by Iteratively Reweighted multivariate alteration detection (IR-MAD) algorithm and support vector machine (SVM) classification. In this study, the change detection analysis has been done using multitemporal Landsat satellite images with 18 years time interval of Shahi Island and a part of the western region of Lake Urmia. The proposed method has two main steps in change monitoring. In the first step, components of change intensities are determined automatically by IR-MAD transformation. In the following, optimized components are selected by applying the kernel principal component analysis (KPCA) on components of change intensities. In the next step, for generating the content of change map, The combination of optimal components is classified by SVM method. For the evaluation performance of the proposed method, in change monitoring, this method was compared with conventional methods such as analysis of the spectral–temporal combination and post classification comparison. The experimental results show that the overall accuracy of the proposed method increased 4.89% and 4.39% compared to that of the spectral-temporal Combination and post classification comparison, respectively. |
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| Keywords: Change monitoring, multitemporal satellite images, reweighted multivariate repeated (IR-MAD) change detection algorithm, support vector machine (SVM) classification |
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Full-Text [PDF 1855 kb]
(1241 Downloads)
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Type of Study: Research |
Subject:
Aerial Photogrammetry
Received: 2016/09/21 | Accepted: 2017/08/26 | Published: 2018/09/22
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