A SpatialSpectral Approach for Deriving High Signal Quality Eigenvectors for Remote Sensing Image Transformations 
Derek Rogge, Martin Bachmann, Benoit Rivard, Allan Aasbjerg Nielsen, Jilu Feng

Abstract  Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformation of the image data onto eigenvectors that comprise physically meaningful spectral properties (signal) can be used to reduce the dimensionality of hyperspectral images as the number of spectrally distinct signal sources composing a given hyperspectral scene is generally much less than the number of spectral bands. Determining eigenvectors dominated by signal variance as opposed to noise is a difficult task. Problems also arise in using these transformations on large images, multiple flightline surveys, or temporal data sets as computational burden becomes significant. In this paper we present a spatialspectral approach to deriving high signal quality eigenvectors for image transformations which possess an inherently ability to reduce the effects of noise. The approach applies a spatial and spectral subsampling to the data, which is accomplished by deriving a limited set of eigenvectors for spatially contiguous subsets. These subset eigenvectors are compiled together to form a new noise reduced data set, which is subsequently used to derive a set of global orthogonal eigenvectors. Data from two hyperspectral surveys are used to demonstrate that the approach can significantly speed up eigenvector derivation, successfully be applied to multiple flightline surveys or multitemporal data sets, derive a representative eigenvector set for the full image data set, and lastly, improve the separation of those eigenvectors representing signal as opposed to noise. 
Keywords  Hyperspectral imaging; spatial and spectral processing; eigenvector transformations 
Type  Journal paper [With referee] 
Journal  International Journal of Applied Earth Observation and Geoinformation 
Year  2014 Month February Vol. 26 pp. 387398 
Publisher  Elsevier 
ISBN / ISSN  DOI:10.1016/j.jag.2013.09.007 
Electronic version(s)  [pdf] 
BibTeX data  [bibtex] 
IMM Group(s)  Image Analysis & Computer Graphics, Geoinformatics 