Abstract
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.
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Jeberson Retna Raj, R., Srinivasulu, S. (2020). Analyzing Heterogeneous Satellite Images for Detecting Flood Affected Area of Kerala. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_78
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DOI: https://doi.org/10.1007/978-981-15-1480-7_78
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