论文标题
基于数字表面模型的照明不变的高光谱图像不变
Illumination invariant hyperspectral image unmixing based on a digital surface model
论文作者
论文摘要
尽管已经开发出许多光谱拆解模型来解决由可变入射照明引起的光谱变异性，但频谱变异性的机制仍不清楚。本文提出了一个未混合模型，称为“照明”频谱不混合（IISU）。 IISU首次尝试使用辐射性高光谱数据和激光衍生的数字表面模型（DSM），以便物理解释Unmixing框架中的可变照明和阴影。从激光雷达衍生的DSM衍生出的事件角，天空因素，可见性，从辐射的角度来看，在不混合过程中明确解释了最终成员的变异性。提出的模型通过直接的优化程序有效地解决。 Unbining结果表明，其他最新的Untrimiging模型尤其是在阴影像素中效果很好。另一方面，提出的模型估计比现有模型更准确的丰度和阴影补偿反射率。
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.
