**作者：**TE-MING TU, CHING-HAI LEE, CHUNG-SHI CHIANG, AND CHIEN-PING CHANG**中文摘要：**In hyperspectral image analysis, determining a distinct material number is an important task for subsequent classification processes. Identifying the number of distinct materials is essentially the same task as determining the intrinsic dimensionality of the imaging spectrometer data. Minimum noise fraction (MNF) transformation or noise-adjusted principal component analysis (NAPCA) is a highly effective means of determining the inherent dimensionality of image data. However, inaccuracy in the noise estimation degrades the validity of this estimation. To effectively resolve this problem, this work presents a novel visual disk (VD) approach which incorporates the NAPCA method into a transformed Gerschgorin disk (TGD) approach. By means of multiple linear regression, Gerschgorin disks in VD can be formed into two distinct, non-overlapping collections; one for signals and the other for noises. Hence, the number of distinct materials can be visually determined by counting the number of Gerschgorin disks for signals. In addition, the VD approach is evaluated based on both simulated and imaging spectrometer data sets collected by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). Experimental results demonstrate that the method proposed herein can be used to effectively solve the intrinsic dimensionality problem.**英文摘要：**--**中文關鍵字：**noise-adjusted principal components analysis, transformed Gerschgorin disk approach, visual disk approach**英文關鍵字：**--