NDLI: A comparative analysis of remote sensing image classification techniques

Content Provider	IEEE Xplore Digital Library
Author	Sisodia, P.S. Tiwari, V. Kumar, A.
Copyright Year	2014
Description	Author affiliation: Electron. & Commun., Manipal Univ. Jaipur, Jaipur, India (Tiwari, V.) \|\| Comput. Sci. & Technol., Manipal Univ. Jaipur, Jaipur, India (Sisodia, P.S.; Kumar, A.)
Abstract	In this paper, we have compared the accuracy of four supervised classification as Mahalanobis, Maximum Likelihood Classification (MLC), Minimum distance and Parallelepiped classification with remote sensing Landsat images of different time period and sensors. We have used Landsat Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper+ (ETM+) images of 1972, 1998 and 2013 respectively of Jaipur district, Rajasthan, India. Accuracy has been calculated using Producer accuracy, User accuracy, Overall accuracy and Kappa statistics. We have found that remote sensing images with a different time period and sensors, when classified with supervised algorithms produced different results. Minimum distance classification produced better accuracy with Landsat MSS image than other three classifications while Maximum Likelihood Classification produced better accuracy with Landsat TM and ETM+ images than other three classifications.
Starting Page	1418
Ending Page	1421
File Size	1704248
Page Count	4
File Format	PDF
ISBN	9781479930784
e-ISBN	9781479930807
DOI	10.1109/ICACCI.2014.6968245
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-24
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Earth Maximum likelihood estimation Accuracy Satellites Landsat MSS Jaipur TM ETM+ Remote sensing Image enhancement Supervised classification Image classification
Content Type	Text
Resource Type	Article

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