摘要
The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.
利用扩散映射所诱导出的扩散几何坐标对高光谱影像低维可视化表示,这种非线性维度约简的表示方法能够得到高光谱影像紧凑而富含信息量的可视化表示结果。通过对海量高光谱影像中每个高光谱观测向量进行局部搜索,仅考虑局部的邻接性和局部的相似度,构造出该高光谱影像对应的近邻图;对近邻图进行合适的规范化,得到该高光谱影像对应的扩散算子,相当于利用该扩散算子对高光谱特征空间模拟出马尔可夫随机游走.因此这样的构造较好地把握了高光谱影像内蕴的几何信息,与传统的基于主成分分析的线性降维表示方法相比,由扩散算子的特征分解所诱导出的扩散几何坐标能够给出更好的表示效果,富含更多的信息.对于大尺度的全景高光谱影像,利用构造"骨干"扩散几何坐标系的方法,其计算的时间复杂性和空间需求都是可接受的.实验也表明,选择合适的对称化方法规范扩散算子对于最终的高光谱影像表示有重要的影响.
基金
The National Key Technologies R & D Program during the 11th Five-Year Plan Period (No.2006BAB15B01)
