Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, Kingdom of Saudi Arabia.
Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, Kingdom of Saudi Arabia.
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DOI：
10.4197/ear.20-1.9
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发表时间：
2009-01-01
期刊：
影响因子：
--
通讯作者：
Hames, A. S.
中科院分区：
文献类型：
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作者：
Al-Ahmadi, F. S.;Hames, A. S.
利用遥感技术从沙特阿拉伯偏远干旱地区的卫星图像中提取一些重要的空间可变参数，如土地覆盖和土地利用(LCLU)。四种不同的无监督分类技术(ISODATA)和有监督分类技术(最大似然、马氏距离和最小距离)被应用于沙特阿拉伯的三个子流域，用于原始TM5图像的分类。然后对绘制的地图进行视觉上的相互比较，并利用地面事实进行准确性评估。结果表明，最大似然法的结果最好，最小距离法和马氏距离法都高估了农地和郊区。尽管由于卫星图像的低分辨率(90m)而遗漏了一些不重要的特征，但从开发的地图中自动提取的参数与现场观测之间发现了很好的一致性。
Remote sensing (RS) technologies was utilized to extract some of the important spatially variable parameters, such as land cover and land use (LCLU), from satellite images for remote arid areas in Saudi Arabia. Four different classification techniques unsupervised (ISODATA), and supervised (Maximum likelihood, Mahalanobis Distance, and Minimum Distance) are applied in three sub-catchments in Saudi Arabia for the classification of the raw TM5 images. The developed maps are then visually compared with each other and accuracy assessments utilizing ground-truths are undertaken. It was found that the Maximum likelihood method gave the best results and both Minimum distance and Mahalanobis distance methods overestimated agriculture land and suburban areas. In spite of missing few insignificant features due to the low resolution of the satellite images (90 m), good agreement between parameters extracted automatically from the developed maps and field observations was found.