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Analyzing Heterogeneous Satellite Images for Detecting Flood Affected Area of Kerala

  • R. Jeberson Retna RajEmail author
  • Senduru Srinivasulu
Conference paper
  • 18 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)

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.

Keywords

Change detection Satellite images Classification Floods 

References

  1. 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
  2. 2.
    Mei, A., C. Manzo, G. Fontinovo, C. Bassani, A. Allegrini, and F. Petracchini. 2015. Assessment of Land Cover Changes in Lampedusa Island (Italy) Using Landsat TM and OLI Data. African Earth Sciences.  https://doi.org/10.1016/j.jafrearsci.2015.05.014.CrossRefGoogle Scholar
  3. 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. 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
  5. 5.
    Nurwandaa, Atik, Alinda Fitriany Malik Zainb, and Ernan Rustiadic. 2016. Analysis of Land Cover Changes and Landscape Fragmentation in Batanghari Regency, Jambi Province. Procedia-Social and Behavioral Sciences 227: 87–94.CrossRefGoogle Scholar
  6. 6.
    Gounaridis, Dimitrios, and Sotirios Koukoulas. 2016. Urban Land Cover Thematic Disaggregation, Employing Datasets from Multiple Sources and Random Forests Modeling. International Journal of Applied Earth Observation and Geoinformation 51: 1–10.CrossRefGoogle Scholar
  7. 7.
    Phiria, Darius, Justin Morgenrotha, Cong Xua, and Txomin Hermosilla. 2018. Effects of Pre-processing Methods on Landsat OLI-8 Land Cover Classification using OBIA and Random Forests Classifier. International Journal Application Earth Obs Geoinformation 73: 170–178.CrossRefGoogle Scholar
  8. 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. 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. 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. 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
  12. 12.
  13. 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. 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. 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. 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. 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

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Information TechnologySathyabama Institute of Science and TechnologyChennaiIndia

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