Determination of data dimensionality in hyperspectral imagery

• Main Authors: Chung-Cheng Kong, 龔中正
• Other Authors: Te-Ming Tu
• Format: Others
• Language: zh-TW
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/11590229045988183433

碩士 === 中正理工學院 === 電機工程研究所 === 87 === ABSTRACT In hyperspectral image analysis, the determination of distinct material number is an important problem for subsequent processing. Essentially, the problem of finding the number of distinct materials is the same as determining the intrinsic dimensionality of the imaging spectrometer data. Minimum noise fraction (MNF) transformation or noise-adjusted principal component analysis (NAPCA) is frequently used to determine the inherent dimensionality for remote sensing images in the past. However, these approaches are limited primarily in that the noise must be accurately estimated from the data or a priori. Inaccurately estimating the noise seriously degrades the validity of the calculated dimensionality. In order to solve this problem validly, we will apply two methods for remote sensing images. First, a visual disk (VD) approach is proposed in this thesis which incorporates the NAPCA method into a transformed Gerschgorin disk (TGD) approach. By the multiple linear regression, Gerschgorin disk in VD can be formed into two distinct, non-overlapped collections, one for signals and the other for noises. Hence the number of distinct materials can be determined visually by counting the number of Gerschgorin disk for signals. The next, we apply NAPCA to partition data space to resolve the inaccuracy of the noise estimation and properly estimate the data dimensionality. This approach is referred to herein as PNAPCA. In contrast to the PCA-based approaches which considers interrelationships within a set of variables, PNAPCA focuses on the relationship between two distinct subspaces which are partitioned from the data space of the original image by a simultaneous transformation. This partitioning causes the gap between the group of eigenvalues for signal plus noise and noise only to become larger than all other PCA-based approaches. The number of endmembers can then be determined by a designed union-intersection margin testing (UIMT). In addition, the performance of both VD and PNAPCA are assessed by two experiments using simulated and real imaging spectrometer data sets collected by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). Experimental results demonstrate that the two proposed methods can effectively determine the intrinsic dimensionality of remote sensing images.