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PROSPECTIVE OF HIGH RESOLUTION WORLDVIEW-2 SATELLITE DATA FOR GEOSPATIAL SURFACE FACIES MAPPING OF AN ALPINE GLACIER

机译:高分辨率的WALLOWVIEW-2卫星数据在阿尔卑斯山冰川表面空间相映射中的应用

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Alpine glaciers are dynamic in nature; and are thus seen as sensitive indicators of climate change. The study of glaciers with respect to these changes must be carried out by first mapping the glacier surface facies. This study has attempted to map the available range of surface facies of an unnamed glacier from the Chandra Basin, in the Great Himalayan Range, Himachal Pradesh. The classification of these facies has been carried out using object-based and pixel-based approaches. Conventional glacier facies classification has usually utilized data acquired in the melt season. This study however has endeavored to map facies on data acquired during early winter. WorldView-2 high-resolution imagery has been used to develop customized spectral indices using the new bands in its multispectral range. Error matrices were utilized to assess the classification accuracies. The object-based approach has been found to have an overall accuracy of 88.33%. Two pixel-based classifiers were utilized, yielding overall accuracies of 81.67% and 78.33% respectively. The highest kappa statistics obtained for the object-based strategy is 0.86 and the pixel based classifiers delivered Kappa statistics of 0.78 and 0.74 respectively. The results clearly indicate that the object-based classification is superior to the pixel based classification methods.
机译:高山冰川自然是动态的。因此被视为气候变化的敏感指标。关于这些变化的冰川研究必须首先绘制冰川表面相来进行。这项研究试图绘制喜马al尔邦大喜马拉雅山脉钱德拉盆地未命名冰川的表面相可用范围。这些相的分类已使用基于对象和基于像素的方法进行。传统的冰川相分类通常利用在融化季节获得的数据。但是,这项研究致力于将初冬期间获得的数据的相图进行映射。 WorldView-2高分辨率图像已被用于在其多光谱范围内使用新波段来开发自定义光谱指数。使用误差矩阵来评估分类准确性。已发现基于对象的方法的总体准确性为88.33%。使用了两个基于像素的分类器,总准确度分别为81.67%和78.33%。针对基于对象的策略获得的最高kappa统计值为0.86,基于像素的分类器分别提供的kappa统计值为0.78和0.74。结果清楚地表明,基于对象的分类优于基于像素的分类方法。

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