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首页> 外文会议>Next-Generation Spectroscopic Technologies >Shadow Removal from VNIR Hyperspectral Remote Sensing Imagery with Endmember Signature Analysis
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Shadow Removal from VNIR Hyperspectral Remote Sensing Imagery with Endmember Signature Analysis

机译：通过终点签名分析从VNIR高光谱遥感图像中删除VNIR高光谱遥感图像

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摘要

This study aims to develop an effective regional shadow removal algorithm using rich spectral information existing in hyperspectral imagery. The proposed method benefits from spectral similarity of shadow and neighboring nonshadow pixels regardless of the intensity values. Although the shadow area has lower reflectance values due to inadequacy of incident light, it is expected that this area contains similar spectral characteristics with nonshadow area. Using this assumption, the endmembers in both shadowed and nonshadow area are extracted by Vertex Component Analysis (VCA). On the other hand, HySime algorithm overcomes estimating number of endmembers, which is one of the challenging parts in hyperspectral unmixing. Therefore, two sets of endmembers are extracted independently for both shadowed and nonshadow area. The proposed study aims at revealing the relation between these two endmember sets by comparing their pairwise similarities. Finally, reflectance values of shadowed pixels are re-calculated separately for each spectral band of hyperspectral image using this information.
机译：本研究旨在使用高光谱图像中存在的丰富光谱信息开发有效的区域阴影去除算法。所提出的方法从阴影的光谱相似性和相邻的非线性像素的频谱相似性，无论强度值如何。虽然由于入射光不足，阴影区域具有较低的反射值，但预计该区域包含与非地球区域相似的光谱特性。使用这种假设，通过顶点分量分析（VCA）提取阴影和非角色区域中的终端终端。另一方面，Hysime算法克服了估计终端数的数量，这是高光谱解波中的具有挑战性的零件之一。因此，对于阴影和非地球区域，可以独立提取两组终端。拟议的研究旨在通过比较它们的成对相似性来揭示这两个终点集之间的关系。最后，使用该信息对每个频带图像的每个光谱频带单独计算阴影像素的反射值。

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