Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Source number estimators using transformed Gerschgorin radii
IEEE Transactions on Signal Processing
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Minimumnoise fraction (MNF) transformation or noise-adjusted principalcomponent analysis (NAPCA) is frequentlyused to determine the inherent dimensionality for remote sensingimages. However, these approaches are limited primarily in thatthe noise must be accurately estimated from the data or a priori.Inaccurately estimating the noise seriously degrades the validityof the calculated dimensionality. In this work, we apply NAPCAto a partitioned data space to resolve the inaccuracy of thenoise estimation and properly estimate the data dimensionality.This approach is referred to herein as PNAPCA. In contrast tothe PCA-based approaches which consider interrelationships within a set of variables, PNAPCA focuses on the relationship between two distinct subspaces which are partitioned from thedata space of the original image by a simultaneous transformation.This partitioning causes the gap between the group of eigenvaluesfor signal plus noise and noise only to become larger than allother PCA-based approaches. The number of endmembers can thenbe determined by a designed union-intersection margin testing(UIMT). In addition, the performance of PNAPCA is assessedby two experiments using simulated and real imaging spectrometerdata sets collected by the Airborne Visible Infrared ImagingSpectrometer (AVIRIS). Experimental results demonstrate thatthe proposed method can effectively determine the intrinsic dimensionalityof remote sensing images.