期刊名称：Journal of Computational Environmental Sciences
印刷版ISSN：2356-7279
电子版ISSN：2314-8292
出版年度：2015
卷号：2015
DOI：10.1155/2015/903465
出版社：Hindawi Publishing Corporation
摘要：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.
