Abstract
Chennai is a metropolitan city in India. It has many lakes and reservoirs which are changing due to urbanization. Remote sensing and GIS techniques are widely used for water body extraction and water body change detection. This study evaluates water body extraction from satellite images of Chennai city using machine learning methods. Many classifiers are trained to extract water bodies from satellite images. Landsat 5 images of Chennai were taken from USGS Earth Explorer for the year 2009. The study aims to compare the classification results of different machine learning algorithms such as J48 decision tree, naive Bayes, multilayer perceptron, k-nearest neighbor, iso-cluster and random forest in extracting the water bodies. The tools used are ArcGIS for geospatial analysis, Weka tool for classification and R for the visual interpretation of the results. The results illustrate that naive Bayes classifier is able to identify lake regions better when compared to all other classifiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Paul, A., Chowdary, V.M., Dutta, D., Sharma, J.R.: Standalone open-source GIS-based tools for land and water resource development plan generation. In: Environment and Earth Observation, pp. 23–34 Springer, Cham (2017)
Balaji, S.A., Geetha, P., Soman, K.P.: Change detection of forest vegetation using remote sensing and GIS techniques in Kalakkad Mundanthurai tiger reserve-(a case study). Indian J. Sci. Technol. 9(30) (2016)
Lillesand, T., Kiefer, R.W., Chipman, J.:Â Remote Sensing and Image Interpretation. Wiley (2014)
Kanevski, M., Pozdnukhov, A., Timonin, V.: Machine learning algorithms for geospatial data. Appl. Softw. Tools (2008)
Karthi, R., Rajendran, C., Rameshkumar, K.: Neighborhood search assisted particle swarm optimization (npso) algorithm for partitional data clustering problems. In: International Conference on Advances in Computing and Communications, pp. 552–561. Springer, Berlin, Heidelberg. (2011, July)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2016)
Rokni, K., Ahmad, A., Selamat, A., Hazini, S.: Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sens. 6(5), 4173–4189 (2014)
Du, Z., Bin, L., Ling, F., Li, W., Tian, W., Wang, H., Zhang, X.: Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China. J. Appl. Remote. Sens. 6(1), 063609 (2012)
Elsahabi, M., Negm, A., El Tahan, A.H.M.: Performances evaluation of surface water areas extraction techniques using Landsat ETM + data: case study Aswan High Dam Lake (AHDL). Procedia Technol. 22, 1205–1212 (2016)
Acharya, T.D., Lee, D.H., Yang, I.T., Lee, J.K.: Identification of water bodies in a Landsat 8 OLI image using a j48 decision tree. Sensors 16(7), 1075 (2016)
Yang, Y., Liu, Y., Zhou, M., Zhang, S., Zhan, W., Sun, C., Duan, Y.: Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach. Remote Sens. Environ. 171, 14–32 (2015)
Zhai, K., Wu, X., Qin, Y., Du, P.: Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. Geo-Spat. Inf. Sci. 18(1), 32–42 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rithin Paul Reddy, K., Srija, S.S., Karthi, R., Geetha, P. (2020). Evaluation of Water Body Extraction from Satellite Images Using Open-Source Tools. In: Thampi, S., et al. Intelligent Systems, Technologies and Applications. Advances in Intelligent Systems and Computing, vol 910. Springer, Singapore. https://doi.org/10.1007/978-981-13-6095-4_10
Download citation
DOI: https://doi.org/10.1007/978-981-13-6095-4_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6094-7
Online ISBN: 978-981-13-6095-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)