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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Comparative Analysis of Different Land Use–Land Cover Classifiers on Remote Sensing LISS-III Sensors Dataset

verfasst von : Ajay D. Nagne, Rajesh Dhumal, Amol Vibhute, Karbhari V. Kale, S. C. Mehrotra

Erschienen in: Computational Intelligence in Data Mining

Verlag: Springer Singapore

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Abstract

Determination and identification of land use–land cover (LULC) of urban area have become very challenging issue in planning a city development. In this paper, we report application of four classifiers to identify LULC using remote sensing data. In our study, LISS-III image dataset of February 2015, obtained from NRSC Hyderabad, India, for the region of Aurangabad city (India) has been used. It was found that all classifiers provided similar results for water body, whereas significant differences were detected for regions related to residential, rock, barren land and fallow land. The average values from these four classifiers are satisfactory in agreement with Toposheet obtained from the Survey of India.

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Vorheriges Kapitel Conditioned Slicing of Aspect-Oriented Program
Nächstes Kapitel Review on Assistive Reading Framework for Visually Challenged
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Metadaten
Titel
Comparative Analysis of Different Land Use–Land Cover Classifiers on Remote Sensing LISS-III Sensors Dataset
verfasst von
Ajay D. Nagne
Rajesh Dhumal
Amol Vibhute
Karbhari V. Kale
S. C. Mehrotra
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Computational Intelligence in Data Mining

Print ISBN: 978-981-10-3873-0

Electronic ISBN: 978-981-10-3874-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-3874-7_49