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通过非线性分解去除高光谱阴影
算法
2026/09/21
摘要：去除遥感高光谱图像中经常出现的阴影对于增强数据的可解释性和进一步的目标分析都很重要。文献中已经提出 了使用线性混合模型的基于光谱解混的阴影去除方法。然而，产生阴影的物体也可能引入光散射，而光子的高阶相互作 用会导致非线性。本文将非线性高光谱解混集成到基于解混的阴影去除中，并研究了在该方法中应用典型非线性算法的 效果。根据对实验室创建的真实数据和实际机载数据的应用结果，验证了非线性解混在高光谱阴影去除中的实用性。
关键词：高光谱成像、非线性光谱解混、阴影去除。
期刊：GEOSCIENCE AND REMOTE SENSING LETTERS
DOI：10.1109/LGRS.2020.2987353
设备：GaiaField
Hyperspectral Shadow Removal via Nonlinear Unmixing
Abstract：Removing shadows that are often present in remotely sensed hyperspectral images is important for both enhancing the interpretability of the data and further target analysis. Shadow removal approaches based on spectral unmixing have been proposed in the literature using the linear mixture model. However, objects that produce shadows may also introduce light scattering, and the higher order interactions of photons can cause nonlinearity. This letter integrates the nonlinear hyperperspectral unmixing into the unmixing-based shadow removal, and the effects of applying typical nonlinear algorithms within the approach are investigated. The usefulness of nonlinear unmixing in hyperspectral shadow removal is verified based on the results of applications to both laboratory- created real data and actual airborne data.
Keywords：Hyperspectral imaging, nonlinear spectral unmixing, shadow removal.
