利用遥感图像进行岩性分类,是遥感地质应用的重要方面之一.本文运用ASTER DEM提取地形因子,并与原始的光谱图像相结合用于遥感图像的岩性单元分类.文章分析了不同尺度的地形因子对岩性单元分类的作用,并进一步分析和比较各种地形因子对岩性单元分类的作用.结果表明,在岩性单元分类过程中加入不同的地形因子可不同程度地提高岩性单元分类精度,加入DEM信息,可得到更高的分类精度.将地形信息和光谱信息综合,是一种有效 的岩性单元分类方法,尤其是对植被覆盖区域.
Lithologic unit mapping from remote sensing imagery is one of the important aspects in geologic remote sensing applications. Traditional lithologic unit mapping can be a time-consuming, costly and challenging endeavour when large areas need to be investigated, where terrain is remote and difficult to access and where the geology is highly variable over short distances. Digital classification using Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) imagery for geological purposes often gives poor results, especially for the imagery with spatial and spectral limitations and areas with dense vegetation cover. The use of ancillary information may help to improve lithological discrimination. To improve classification accuracy, spectral data have been combined with ancillary data, such as topographic variables and DEM data. These ancillary data, which was used to add information about the geomorphologic nature of the different geological units, have been used in classification processing to enhance image quality. So, geological classifications were often conducted using various kinds of integrated spectral and topographic variables. In this paper, the topographic variables such as slope and aspect at different scales were extracted from ASTER DEM, and were then combined with original multispectral image including ASTER three VNIR bands and six SWIR bands in image classification for lithologic unit mapping. In order to evaluate the performance of topographic variables in lithologic unit mapping, topographic variables derived at multiple scales were analyzed in terms of images classification accuracy. The experimental results showed that, the inclusion of topographic variables in lithologic unit classification improved the classification accuracy in different degrees. In particular, when DEM data were included in image classification, the overall accuracy could be improved the most. Therefore, the inclusion of topographic variables in image classification is an effective method for lithologic unit mapping, especially in vegetated areas.
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