Random Forest Classifier for Extracting Water bodies from Pansharpened Image to Detect Surface Water Changes
K. Kalaivani1, Asnath Victy Phamila2, Sathish Kumar Selvaperumal3
1K.Kalaivani*, Research Scholar, Computing Science and Engineering, VIT University, Assistant Professor, Department of Computer Science and Engineering, Vels Institute of Science, Technology and Advance Studies, Chennai, India.
2Asnath Victy Phamila, Associate Professor, School of Computing Science and Engineering, VIT University, Chennai, India.
3Sathish Kumar Selvaperumal, Associate Professor, Asia Pacific University Technology Park Malaysia, Kuala Lumpur, Malaysia.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 4910-4912 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2039109119/2019©BEIESP | DOI: 10.35940/ijeat.A2039.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Change detection from time series multispectral Landsat imagery has been an active research in remote sensing for several years to monitor the ecosystem, environment, climate and so on. This study is focused on detecting the changes in surface water by the integration of fusion and image classification techniques in multi-temporal multispectral Landsat images. The panchromatic band and the multispectral band of Landsat OLI and TM images respectively, were fused using undecimated wavelet transform to get the pan-sharpened image. Then classification techniques like Maximum Likelihood, Support Vector Machine, Artificial Neural Network and Random Forest were employed for extracting the water pixels and changed pixels. The performances of these classification techniques were analyzed based on metrics such as overall error, commission error, precision, recall, overall accuracy, kappa coefficients and the results show that the application of random forest classifier on pansharpened image outperforms in extracting the water pixels and also in highlighting the changes with maximum accuracy.
Keywords: Change detection, image classification, pansharpening, random forest, surface water.