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

机译：使用后分类增强功能提高Landsat数据的土地利用和土地覆盖分类的准确性

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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.
机译：对遥感影像进行分类以获取可靠，准确的土地利用和土地覆盖（LULC）信息仍然是一个挑战，这取决于许多因素，例如景观的复杂性，选择的遥感数据，图像处理和分类方法等。本文旨在从澳大利亚新南威尔士州下亨特地区的Landsat影像中提取可靠的LULC信息。经典最大似然分类器（MLC）首次用于对1985年的Landsat-MSS和1995年和2005年的Landsat-TM进行分类。确定的主要LULC为林地，牧场/灌丛，葡萄园，建筑物和水体。通过使用辅助数据和基于知识的逻辑规则应用分类后校正（PCC），总体分类精度从1985年地图的约72％提高到91％，1995年地图从76％提高到90％，而1995年地图从79％提高到87％ 2005年地图。由于PCC，改进的总体Kappa统计数据在1985年的地图上为0.88，在1995年的地图上为0.86，在2005年的地图上为0.83。通过McNemar的检验评估，PCC地图的准确性比其对应的MLC地图更高。就其在该地区土地变化建模中的潜在用途而言，LULC地图的分类准确性的整体改善是重要的。

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