Illumination Invariant Hyperspectral Image Unmixing Based on a Digital Surface Model
Illumination Invariant Hyperspectral Image Unmixing Based on a Digital Surface Model
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基于数字表面模型的光照不变高光谱图像分解
DOI：
10.1109/tip.2020.2963961
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发表时间：
2020-01
影响因子：
10.6
通讯作者：
Tatsumi Uezato;N. Yokoya;Wei He
中科院分区：
文献类型：
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作者：
Tatsumi Uezato;N. Yokoya;Wei He
尽管已经开发了许多光谱分解模型来解决由可变入射照度引起的光谱变化，但光谱变化的机制仍然不清楚。提出了一种光照不变光谱分解模型（IISU）。IISU首次尝试使用辐射率高光谱数据和LiDAR衍生的数字表面模型（DSM），以便在物理上解释解混框架中的可变照明和阴影。入射角，天空因素，来自太阳的可见度从激光雷达衍生DSM支持明确的解释端元的变化，从辐射的角度来看，在解混过程中。提出的模型有效地解决了一个简单的优化过程。解混结果表明，其他国家的最先进的解混模型并不工作，特别是在阴影像素。另一方面，所提出的模型估计更准确的丰度和阴影补偿反射比现有的模型。
Although many spectral unmixing models have been developed to address spectral variability caused by variable incident illuminations, the mechanism of the spectral variability is still unclear. This paper proposes an unmixing model, named illumination invariant spectral unmixing (IISU). IISU makes the first attempt to use the radiance hyperspectral data and a LiDAR-derived digital surface model (DSM) in order to physically explain variable illuminations and shadows in the unmixing framework. Incident angles, sky factors, visibility from the sun derived from the LiDAR-derived DSM support the explicit explanation of endmember variability in the unmixing process from radiance perspective. The proposed model was efficiently solved by a straightforward optimization procedure. The unmixing results showed that the other state-of-the-art unmixing models did not work well especially in the shaded pixels. On the other hand, the proposed model estimated more accurate abundances and shadow compensated reflectance than the existing models.