Analyzing Heterogeneous Satellite Images for Detecting Flood Affected Area of Kerala
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Kerala flood is one of the most disastrous in recent years which affects millions of people’s lives into standstill and thousands of people lost their houses and properties. Landslides and water inundation really hit the normal life of the people. The effects of climate change influences the environment by changing landscape, incessant rainfall, raise of temperature, failure of monsoon, etc. in this paper, the change detection of Kerala flood is analyzed and compared. Two different satellite images of before and after flood are considered and the changes in the flood-affected area are detected. The satellite image is co-registered, calibrated and geometric correction made for processing. The pre-processing algorithms are used to filter the speckle noise and making the image as noise-free. The image is analyzed and classified with supervised and unsupervised algorithms. The unsupervised K means algorithm and supervised algorithm such as Random forest, K-Nearest Neighborhood (KNN), KDTree-KNN, Maximum Likelihood (ML) and Minimum Distance (MD) classifiers are applied and the performance of the algorithms are compared. Finally, the changes in the image are demarcated and analyzed.
KeywordsChange detection Satellite images Classification Floods
- 1.Wei, Zhao, Zhirui Wang, and Maoguo Gong. 2017. Discriminative Feature Learning for Unsupervised Change Detection in Heterogeneous Images Based on a Coupled Neural Network. IEEE Transactions on Geoscience and Remote Sensing 55 (12).Google Scholar
- 3.Rejaur, Md Rahman, and Praveen K. Thakur. 2018. Detecting, Mapping and Analysing of Flood Water Propagation using Synthetic Aperture Radar (SAR) Satellite Data and GIS: A Case Study from the Kendrapara District of Orissa State of India. The Egyptian Journal of Remote Sensing and Space Sciences, 21 (1): S37–S41.Google Scholar
- 4.dos Santos, J.A., C.D. Ferreira, R.D.S. Torres, M.A. Gonçalves, and R.A.C. Lamparelli. 2011. A Relevance Feedback Method Based on Genetic Programming for Classification of Remote Sensing Images. Information Sciences 181: 2671–2684.Google Scholar
- 8.Rao Zahid, Khalil, and Saad-ul-Haque. InSAR Coherence-Based Land Cover Classification of Okara, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, http://dx.doi.org/10.1016/j.ejrs.2017.08.005.
- 9.Caterina, Samela, Raffaele Albano, Aurelia Sole, and Salvatore Manfreda. A GIS Tool for Cost-Effective Delineation of Flood-Prone Areas. Computers, Environment and Urban Systems, https://doi.org/10.1016/j.compenvurbsys.2018.01.013.
- 10.Himabindu, G., and M. Ramakrishna Murty et al. 2018. Classification of Kidney Lesions Using Bee Swarm Optimization. International Journal of Engineering &Technology 7 (2.33): 1046–1052.Google Scholar
- 11.Himabindu, G., and M. Ramakrishna Murty et al. 2018. Extraction of Texture Features and Classification of Renal Masses from Kidney Images. International Journal of Engineering &Technology 7(2.33): 1057–1063.Google Scholar
- 13.Ran, He, Bao-Gang Hu, Wei-Shi Zheng, and Xiang-Wei Kong. 2011. Robust Principal Component Analysis Based on Maximum Correntropy Criterion. IEEE Transactions on Image Processing 20 (6): 1485–1494.Google Scholar
- 14.Yaoguo, Zheng, Xiangrong Zhang, Biao Hou, and Ganchao Liu. 2014. Using Combined Difference Image and k-Means Clustering for SAR Image Change Detection. IEEE Geoscience and Remote Sensing Letters 11 (3): 691–695.Google Scholar
- 15.Ham, J., Yangchi Chen, M.M. Crawford, and J. Ghosh. 2005. Investigation of the Random Forest Framework for Classification of Hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 43 (3): 492–501.Google Scholar
- 16.Yu, Zhiwen, Hantao Chen, Jiming Liu, Jane You, Hareton Leung, and Guoqiang Han. 2016. Hybrid k-Nearest Neighbor Classifier. IEEE Transactions on Cybernetics 46(6): 1263–1275.Google Scholar
- 17.Bruzzone, L., and D.F. Prieto. 2001. Unsupervised Retraining of a Maximum Likelihood Classifier for the Analysis of Multitemporal Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 39 (2): 456–460.Google Scholar