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The effect of contaminated snow reflectance using hyperspectral remote sensing - a review

机译：高光谱遥感对雪反射率的影响-综述

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Snow is composed of small crystalline ice particles consisting of multitude of snowflakes that fall from clouds. This review paper highlights the scope and nature of the research work done in North West Himalayan region. Spectral signatures were collected for varying snow grain size, contamination, adjacency factors and other ambient objects. The retrieval of snow parameters such as grain size, contamination, spectral albedo using high resolution imaging data at different wavelength are discussed in this paper. Wavelengths 550, 1240 and 1660 nm are found to be useful wavelength for discriminating different snow feature. Spectral unmixing (SU) or the disintegration of individual spectra into a mixture of a small number of end members represents the spectra of pure and contaminated components. Many linear SU techniques exploit this notion in a way or another. For instance, where the pixel purity index algorithm projects the spectra of every pixel onto random vectors in spectral space, and tags the extremities. The spectra that got tagged the most is considered as end members. The N-findR algorithm searches for the largest volume simplex via an iterative procedure, and assigns the vertices of this simplex as end members. These algorithms of end member extraction are based on the assumption that spectra of pure pixel exist in data and form the extremes of a simplex embedded in the data cloud. But, in reality this is often not the case as with multiple scattering in wet environments, secondary reflections through vegetation canopies or between fuzzy surface materials. Nonlinear algorithm is made upon an assumption that the pixel reflectance results from nonlinear function of the abundance vectors associated with the pure spectra of snow with ambiguity of unknown spectral signatures of the pure snow and nonlinear function.
机译：雪由小的结晶冰粒组成，这些冰粒由从云中掉落的许多雪花组成。这篇评论文章强调了在西北喜马拉雅地区开展的研究工作的范围和性质。收集了不同雪粒大小，污染，邻接因子和其他周围物体的光谱特征。本文讨论了使用不同波长的高分辨率成像数据检索雪参数（例如粒度，污染，光谱反照率）的方法。发现波长550、1240和1660nm对于区分不同的雪特征是有用的波长。光谱解混（SU）或将单个光谱分解为少量端基的混合物表示的是纯组分和受污染组分的光谱。许多线性SU技术都以某种方式利用了这一概念。例如，在像素纯度指标算法中，将每个像素的光谱投影到光谱空间中的随机矢量上，并标记末端。被标记最多的光谱被视为末端成员。 N-findR算法通过迭代过程搜索最大体积的单纯形，并将该单纯形的顶点指定为末端成员。这些末端成员提取算法基于以下假设：数据中存在纯像素的光谱，并且形成嵌入数据云中的单纯形的极端。但是，实际上，在潮湿环境中发生多次散射，通过植被冠层或模糊表面材料之间产生二次反射的情况通常并非如此。非线性算法是基于以下假设进行的：像素反射率是由与纯雪光谱关联的丰度矢量的非线性函数产生的，而纯雪的未知光谱特征和非线性函数的模棱两可。

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