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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WorldView-2卫星数据的潜在地理空间表面相位绘图的卫星数据

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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:高山冰川是动态的;因此被视为气候变化的敏感指标。必须首先通过首先将冰川表面相进行绘制来进行冰川关于这些变化的研究。这项研究试图从喜马拉雅山脉的伟大喜马拉雅山脉从Chandra Basin映射未命名冰川的可用范围。使用基于对象和基于像素的方法进行了这些相的分类。传统的冰川相分类通常使用在熔体季节中获得的数据。然而,本研究致力于映射初期收购数据的地图。 WorldView-2高分辨率图像已被用于使用多光谱范围内的新频段开发定制的光谱索引。使用错误矩阵来评估分类精度。已发现基于对象的方法具有88.33%的整体准确性。利用了两个基于像素的分类剂,产生了81.67%和78.33%的总体精度。基于对象的策略获得的最高kappa统计数据是0.86,并且基于像素的分类器分别传送了0.78和0.74的kappa统计。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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