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Accuracy of crop-shelter thematic maps: A case study of maps obtained by spectral and textural classification of high-resolution satellite images

机译:避难所专题图的准确性:以高分辨率卫星图像的光谱和纹理分类获得的地图为例

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Accuracy assessment procedures are usually recommended in land-use/land-cover change studies to ascertain the quality of thematic maps produced by means of automated image classification methods. In this regard, the error matrix and accompanying accuracy measures (AMs) are widely used. Concerning thematic maps of crop-shelter coverage (CSC), high-resolution remote sensing images have been found to be suitable for the automated classification of greenhouses and several methodologies have been developed. However, methods to report the thematic map accuracy derived from these classifications still need to be standardized. Therefore, this study was undertaken with the aim to provide guidelines for determining a set of AMs suitable to report the accuracy of CSC thematic maps produced at local scale. In the case study, QuickBird RGB-band layers were combined with textural information computed on degraded QuickBird panchromatic layer to perform CSC supervised classifications in a study area located in South-Eastern Sicily (Italy) where protected cultivation is widespread. Statistical tests were performed on a number of AMs computed on the error matrices obtained from the automated image classifications of three different areas selected within the study area. The results of this study showed that user’s accuracy was not influenced by the area selection and, therefore, can be used to report the accuracy of CSC thematic maps obtained by automated image classifications of other areas located within the study area. All the obtained classifications differed from a random one; and the use of textural information generally improved classifications’ accuracy in comparison to that obtained by using only RGB-band layers.
机译:通常,在土地利用/土地覆被变化研究中建议使用准确性评估程序,以确定通过自动图像分类方法制作的专题图的质量。在这方面,误差矩阵和附带的精度度量(AM)被广泛使用。关于农作物遮蔽物的专题图(CSC),已经发现高分辨率遥感图像适合于温室的自动分类,并且已经开发了几种方法。但是,报告来自这些分类的主题地图准确性的方法仍然需要标准化。因此,进行这项研究的目的是为确定一套适合报告当地规模的CSC专题图的准确性的AM提供指导。在案例研究中,将QuickBird RGB波段层与在退化的QuickBird全色层上计算出的纹理信息结合起来,以在位于意大利西西里岛东南部的一个研究区域进行CSC监督的分类,该区域广泛进行了受保护的栽培。对根据从研究区域内选择的三个不同区域的自动图像分类获得的误差矩阵计算出的多个AM进行统计测试。这项研究的结果表明,用户的准确性不受区域选择的影响,因此,可用于报告通过研究区域内其他区域的自动图像分类获得的CSC专题图的准确性。所有获得的分类均不同于随机分类。与仅使用RGB波段图层相比,使用纹理信息通常可以提高分类的准确性。

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