NDLI: Regression tree algorithm for classification of fused multispectral and panchromatic image

Content Provider	IEEE Xplore Digital Library
Author	Shingare, P.P. Hemane, P.M. Dandekar, D.S.
Copyright Year	2014
Description	Author affiliation: EnTC, Dept., Coll. of Eng., Pune, India (Shingare, P.P.; Hemane, P.M.; Dandekar, D.S.)
Abstract	In this paper, classification of satellite image is done to detect vegetation, water, soil, built-up area etc. present in the region of which satellite image is captured. For classification regression tree algorithm is used. Regression tree algorithm uses threshold to detect class of data that gives least mean square error. This threshold is applied to NDVI, NDWI, SAVI, and NDBI to detect vegetation, water, soil and built-up area respectively. For classification Landsat 7 ETM+ multispectral images are used. But they have low spatial resolution. Hence they are fused with panchromatic image which is of high resolution. Image fusion is done using HIS transform, Brovey transform, PCA method, HPF method and wavelet transform. Fused image is given as input to regression tree for classification. Using this method various areas can be detected effectively as compared to the original satellite image. Classified image is compared with reference data to check the accuracy of classified image. It is observed that image fused using HPF method when classified using regression tree algorithm gives more effective results.
Starting Page	2449
Ending Page	2454
File Size	4240362
Page Count	6
File Format	PDF
ISBN	9781479930784
e-ISBN	9781479930807
DOI	10.1109/ICACCI.2014.6968419
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	Regression tree algorithm HIS Wavelet transform Entropy Brovey transform MATLAB Image fusion Principal component analysis PCA HPF
Content Type	Text
Resource Type	Article

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