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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Spherical Stochastic Neighbor Embedding of Hyperspectral Data
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Spherical Stochastic Neighbor Embedding of Hyperspectral Data

机译：高光谱数据的球面随机邻居嵌入

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摘要

In hyperspectral imagery, low-dimensional representations are sought in order to explain well the nonlinear characteristics that are hidden in high-dimensional spectral channels. While many algorithms have been proposed for dimension reduction and manifold learning in Euclidean spaces, very few attempts have focused on non-Euclidean spaces. Here, we propose a novel approach that embeds hyperspectral data, transformed into bilateral probability similarities, onto a nonlinear unit norm coordinate system. By seeking a unit $ ell_{2}$-norm nonlinear manifold, we encode similarity representations onto a space in which important regularities in data are easily captured. In its general application, the technique addresses problems related to dimension reduction and visualization of hyperspectral images. Unlike methods such as multidimensional scaling and spherical embeddings, which are based on the notion of pairwise distance computations, our approach is based on a stochastic objective function of spherical coordinates. This allows the use of an Exit probability distribution to discover the nonlinear characteristics that are inherent in hyperspectral data. In addition, the method directly learns the probability distribution over neighboring pixel maps while computing for the optimal embedding coordinates. As part of evaluation, classification experiments were conducted on the manifold spaces for hyperspectral data acquired by multiple sensors at various spatial resolutions over different types of land cover. Various visualization and classification comparisons to five existing techniques demonstrated the strength of the proposed approach while its algorithmic nature is guaranteed to converge to meaningful factors underlying the data.
机译：在高光谱图像中，寻求低维表示，以便很好地解释隐藏在高维光谱通道中的非线性特征。虽然已经提出了许多用于欧氏空间中的降维和流形学习的算法，但很少有尝试集中在非欧氏空间上。在这里，我们提出了一种将高光谱数据嵌入到非线性单位范数坐标系中的新方法，该方法将转换成双边概率相似性的数据嵌入其中。通过寻找单位$ ell_ {2} $-范数非线性流形，我们将相似性表示编码到易于捕获数据重要规律性的空间上。在其一般应用中，该技术解决了与降维和高光谱图像可视化有关的问题。与基于成对距离计算概念的多维缩放和球面嵌入等方法不同，我们的方法基于球面坐标的随机目标函数。这允许使用出口概率分布来发现高光谱数据中固有的非线性特征。另外，该方法在计算最佳嵌入坐标的同时，直接学习相邻像素图的概率分布。作为评估的一部分，对流形空间进行分类实验，以获取由多个传感器以不同空间分辨率对不同类型的土地覆盖物获取的高光谱数据。与五种现有技术的各种可视化和分类比较证明了该方法的优势，同时保证了其算法性质可以收敛到数据背后的有意义因素。

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