CAJ | 학술논문

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.
이용확산영사소유도출적확산궤하좌표대고광보영상저유가시화표시,저충비선성유도약간적표시방법능구득도고광보영상긴주이부함신식량적가시화표시결과.통과대해량고광보영상중매개고광보관측향량진행국부수색,부고필국부적린접성화국부적상사도,구조출해고광보영상대응적근린도;대근린도진행합괄적규범화,득도해고광보영상대응적확산산자,상당우이용해확산산자대고광보특정공간모의출마이가부수궤유주.인차저양적구조교호지파악료고광보영상내온적궤하신식,여전통적기우주성분분석적선성강유표시방법상비,유확산산자적특정분해소유도출적확산궤하좌표능구급출경호적표시효과,부함경다적신식.대우대척도적전경고광보영상,이용구조"골간"확산궤하좌표계적방법,기계산적시간복잡성화공간수구도시가접수적.실험야표명,선택합괄적대칭화방법규범확산산자대우최종적고광보영상표시유중요적영향.
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.