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Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2018-06-26 , DOI: 10.1016/j.jag.2018.06.014
Darius Phiri , Justin Morgenroth , Cong Xu , Txomin Hermosilla

The application of Landsat satellite imagery in land cover classification is affected by atmospheric and topographic errors, which have led to the development of different correction methods. In this study, moderate resolution atmospheric transmission (MODTRAN) and dark object subtraction (DOS) atmospheric corrections, and cosine topographic correction were evaluated individually and combined in a heterogeneous landscape in Zambia. These pre-processing methods were tested using a combination of object-based image analysis (OBIA) and Random Forests (RF) non-parametric classifier (hereafter referred to as OBIA-RF). This assessment aimed at understanding the combined effects of different pre-processing methods and the OBIA-RF classification method on the accuracy of Landsat operational land (OLI-8) imagery with different spatial resolutions. Here, we used pansharpened and standard Landsat OLI-8 images with 15 and 30 m spatial resolutions, respectively. The results showed that non pre-processed images reached a classification accuracy of 68% for pansharpened and 66% for standard Landsat OLI-8. Classification accuracy improved to 93% (pansharpened) and 86% (standard) when combined MODTRAN and cosine topographic correction pre-processing were applied. The results highlight the importance of pansharpening, as well as atmospheric and topographic corrections for Landsat OLI-8 imagery, when used as input in OBIA classification with the RF classifier.



中文翻译:

使用OBIA和随机森林分类器的预处理方法对Landsat OLI-8土地覆盖分类的影响

Landsat卫星影像在土地覆盖分类中的应用受到大气和地形误差的影响,这导致了不同校正方法的发展。在这项研究中,分别评估了中等分辨率的大气透射率(MODTRAN)和暗物减法(DOS)的大气校正以及余弦形貌校正,并在赞比亚的异质景观中进行了组合。使用基于对象的图像分析(OBIA)和随机森林(RF)非参数分类器(以下称为OBIA-RF)的组合对这些预处理方法进行了测试。这项评估旨在了解不同预处理方法和OBIA-RF分类方法对具有不同空间分辨率的Landsat工作用地(OLI-8)图像准确性的综合影响。这里,我们分别使用了全分辨率锐化图像和标准Landsat OLI-8图像,这些图像的空间分辨率分别为15 m和30 m。结果表明,未经预处理的图像对全锐化的分类精度达到68%,对于标准Landsat OLI-8的分类精度达到66%。当结合使用MODTRAN和余弦形貌校正预处理时,分类精度提高到93%(锐利化)和86%(标准)。这些结果强调了在使用RF分类器将OBIA分类中用作输入时,对Landsat OLI-8影像进行全貌锐化以及进行大气和地形校正的重要性。当结合使用MODTRAN和余弦形貌校正预处理时,分类精度提高到93%(锐利化)和86%(标准)。这些结果强调了在使用RF分类器将OBIA分类中用作输入时,对Landsat OLI-8影像进行全貌锐化以及进行大气和地形校正的重要性。当结合使用MODTRAN和余弦形貌校正预处理时,分类精度提高到93%(锐利化)和86%(标准)。这些结果强调了在使用RF分类器将OBIA分类中用作输入时,对Landsat OLI-8影像进行全貌锐化以及进行大气和地形校正的重要性。

更新日期:2018-06-26
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