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Proceedings Paper

Noise subspace projection approaches to determination of intrinsic dimensionality of hyperspectral imagery
Author(s): Chein-I Chang; Qian Du
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Paper Abstract

Determination of Intrinsic Dimensionality (ID) for remotely sensed imagery has been a challenging problem. For multispectral imagery it may be solvable by Principal Components Analysis (PCA) due to a small number of spectral bands which implies that ID is also small. However, PCA method may not be effective if it is applied to hyperspectral images. This may arise in the fact that a high spectral-resolution hyperspectral sensor may also extract many unknown interfering signatures in addition to endmember signatures. So, determining the ID of hyperspectral imagery is more problematic than that of multispectral imagery. This paper presents a Neyman-Pearson detection theory-based eigen analysis for determination of ID for hyperspectral imagery, particularly, a new approach referred to as Noise Subspace Projection (NSP)-based eigen-thresholding method. It is derived from a noise whitening process coupled with a Neyman- Pearson detector. The former estimates the noise covariance matrix which will be used to whiten the data sample correlation matrix, whereas the latter converts the problem of determining ID to a Neyman-Pearson decision with the Receiver Operating Characteristics (ROC) analysis used as a thresholding technique to estimate ID. In order to demonstrate the effectiveness of the proposed method AVIRIS are used for experiments.

Paper Details

Date Published: 14 December 1999
PDF: 11 pages
Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); doi: 10.1117/12.373271
Show Author Affiliations
Chein-I Chang, Univ. of Maryland/Baltimore County (United States)
Qian Du, Univ. of Maryland/Baltimore County (United States)

Published in SPIE Proceedings Vol. 3871:
Image and Signal Processing for Remote Sensing V
Sebastiano Bruno Serpico, Editor(s)

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