中文摘要：

在高光谱数据降维过程中，通常用虚拟维数来表征数据的本征维数．经典的虚拟维数分析算法主要运用假设检验准则设定特征值门限，通过特征值判定来决定虚拟维数值．但是，在强噪声干扰下，经典算法不能有效分析出虚拟维数值．本文提出了一种噪声抑制的高光谱图像虚拟维数分析方法（NCVD），该算法通过对数据矩阵进行QR分解，减小了算法的运算量；采用滑动噪声检测窗口对噪声成分进行滤除，提高了估计维数的准确性；结合最小二乘算法对判别门限进行修正，使虚拟维数估计结果更具合理性；采用模拟数据和真实数据进行实验，实验结果证明，本文所提算法的可行性和较现有算法的优越性．

英文摘要：

In dimensionality reduction process of hyperspectral data, intrinsic dimension is normally characterized by virtual dimension. Classic algorithm mainly uses hypothesis-testing criterion to set eigenvalue threshold and correspondingly obtains virtual dimension. But under strong noises, it may not estimate very well. A noise constrained virtual dimension （NCVD） analysis method of hyperspectral imagery is proposed in this paper. It decreases the computational complexity by the QR decomposing; improves the accuracy of the estimated dimension by adopting sliding noise detection window to filter the noise; synthesizes the least squares algorithm to modify threshold for reasonable results. The experimental results prove the feasibility and superiority of the proposed algorithm by using simulated and real data.