Table of Contents
Journal of Computational Environmental Sciences
Volume 2015, Article ID 903465, 9 pages
Research Article

Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model

Lab for Spatial Informatics, International Institute of Information and Technology, Gachibowli, Hyderabad, Telangana 500032, India

Received 9 September 2014; Revised 29 December 2014; Accepted 31 December 2014

Academic Editor: Timothy O. Randhir

Copyright © 2015 Kshitij Mishra and P. Rama Chandra Prasad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Extraction of water bodies from satellite imagery has been widely explored in the recent past. Several approaches have been developed to delineate water bodies from different satellite imagery varying in spatial, spectral, and temporal characteristics. The current study puts forward an automatic approach to extract the water body from a Landsat satellite imagery using a perceptron model. Perceptron involves classification based on a linear predictor function that merges few characteristic properties of the object commonly known as feature vectors. The feature vectors, combined with the weights, sum up to provide an input to the output function which is a binary hard limit function. The feature vector in this study is a set of characteristic properties shown by a pixel of the water body. Low reflectance of water in SWIR band, comparison of reflectance in different bands, and a modified normalized difference water index are used as descriptors. The normalized difference water index is modified to enhance its reach over shallow regions. For this study a threshold value of 2 has been proved as best among the three possible threshold values. The proposed method accurately and quickly discriminated water from other land cover features.