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Remote Sens. 2009, 1(3), 330-344; doi:10.3390/rs1030330

Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement

Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW 2006, Australia
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Received: 2 July 2009 / Revised: 14 July 2009 / Accepted: 23 July 2009 / Published: 31 July 2009
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Abstract

Classifying remote sensing imageries to obtain reliable and accurate land use and land cover (LULC) information still remains a challenge that depends on many factors such as complexity of landscape, the remote sensing data selected, image processing and classification methods, etc. The aim of this paper is to extract reliable LULC information from Landsat imageries of the Lower Hunter region of New South Wales, Australia. The classical maximum likelihood classifier (MLC) was first applied to classify Landsat-MSS of 1985 and Landsat-TM of 1995 and 2005. The major LULC identified were Woodland, Pasture/scrubland, Vineyard, Built-up and Water-body. By applying post-classification correction (PCC) using ancillary data and knowledge-based logic rules the overall classification accuracy was improved from about 72% to 91% for 1985 map, 76% to 90% for 1995 map and 79% to 87% for 2005 map. The improved overall Kappa statistics due to PCC were 0.88 for the 1985 map, 0.86 for 1995 and 0.83 for 2005. The PCC maps, assessed by McNemar’s test, were found to have much higher accuracy in comparison to their counterpart MLC maps. The overall improvement in classification accuracy of the LULC maps is significant in terms of their potential use for land change modelling of the region.
Keywords: image classification; Landsat; ancillary data; accuracy; Lower Hunter image classification; Landsat; ancillary data; accuracy; Lower Hunter
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Manandhar, R.; Odeh, I.O.A.; Ancev, T. Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement. Remote Sens. 2009, 1, 330-344.

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