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The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping

机译:高光谱数据的地形归一化:对光谱末端成员的选择和岩性标测的意义

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Compact Airborne Spectrographic Imager (CASI) hyperspectral data is used to investigate the effects of topography on the selection of spectral end members, and to assess whether the topographic correction improves the discrimination of rock units for lithologic mapping. A publicly available Digital Elevation Model (DEM), at a scale of 1:50,000, is used to model the radiance variation of the scene as a function of topography, assuming a Lambertian surface. Skylight is estimated and removed from the airborne data using a dark object correction. The CASI data is corrected on a pixel-by-pixel basis to normalize the scene to a uniform solar illumination and viewing geometry. The results show that topography has the effect of expanding end member clusters at times resulting in the overlap of clusters and that the correction process can effectively reduce the variation in detected radiance due to changes in local illumination. When topographic effects are embedded in the hyperspectral data, methods typically used for the selection of end members, such as the convex hull method, can miss end members or result in the selection of nonrepresentative pixels as end members. Thus, end members selected by some conventional methods are very likely "incomplete" or "nonrepresentative" if the topographic effect is embedded in the data. As shown in this study, the topographic correction can reveal hidden end members and achieve a better representation of end members via the statistical center of isolated clusters. (C) 2003 Elsevier Science Inc. All rights reserved. [References: 49]
机译:紧凑型机载光谱成像仪(CASI)高光谱数据用于研究地形对光谱末端成员选择的影响,并评估地形校正是否可以改善岩性用于岩性制图的判别力。假定朗伯曲面,使用公开比例为1:50,000的数字高程模型(DEM)来建模场景的辐射度随地形变化的函数。使用暗物体校正估计天光并从机载数据中删除。 CASI数据在逐个像素的基础上进行校正,以将场景归一化为均匀的太阳光照和观看几何形状。结果表明,形貌具有有时会扩大端部成员簇的效果,从而导致簇的重叠,并且校正过程可以有效地减少由于局部光照的变化而导致的检测辐射的变化。当地形效果嵌入高光谱数据中时,通常用于选择端部成员的方法(例如凸包法)可能会丢失端部成员或导致选择非代表性像素作为端部成员。因此,如果将地形效果嵌入数据中,则通过某些常规方法选择的最终成员很可能是“不完整”或“不具代表性的”。如本研究所示,地形校正可以揭示隐藏的末端成员,并通过孤立聚类的统计中心更好地表示末端成员。 (C)2003 Elsevier Science Inc.保留所有权利。 [参考:49]

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