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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/10583

Title: Comparison of land cover image classification methods
Authors: Osei, Kingsley Nana
Osei Jnr., Edward Matthew
Adjapong, Adwoa Sarpong
Keywords: Land cover classification
Maximum Likelihood classification
Backpropagation Neural Network classification
Subpixel classification
Issue Date: Apr-2012
Publisher: Journal of Geomatics
Citation: Journal of Geomatics, Vol 6 No.1 April 2012
Abstract: In remote sensing, many methods have been developed for image classification. In this study, three of the methods namely Maximum Likelihood classification (MLC), Backpropagation Neural Network classification (BPNN), and Sub Pixel classification (SP) are used to classify a Landsat ETM+ image of the Ejisu-Juabeng district of Ghana into seven land cover classes and the results are compared. The seven classes identified were forest, forested wetland, open woodland, water, non-forested wetland, grassland and urban. In the comparison, the top 20 (80%-100% composition) per land cover class from the SP is used against the MLC and BPNN classification. The results show that of the two hard classifications (MLC & BPNN), BPNN gave a better result with an overall accuracy of 92.5 % compared with that of MLC with an accuracy of 78.95%. The SP classification however, gave mixed results although for land cover classes such as forest and forested wetland that are homogeneous in nature, the representations were good. Over all the BPNN classification gave the best representation of the land cover classes in the study area.
Description: An article published by Journal of Geomatics, Vol 6 No.1 April 2012
URI: http://hdl.handle.net/123456789/10583
Appears in Collections:College of Engineering

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