7 May 2007 Hyperspectral imagery transformations using real and imaginary features for improved classification
Author Affiliations +
Abstract
Several studies have reported that the use of derived spectral features, in addition to the original hyperspectral data, can facilitate the separation of similar classes. Linear and nonlinear transformations are employed to project data into mathematical spaces with the expectation that the decision surfaces separating similar classes become well defined. Therefore, the problem of discerning similar classes in expanded space becomes more tractable. Recent work presented by one of the authors discusses a dimension expansion technique based on generating real and imaginary complex features from the original hyperspectral signatures. A complex spectral angle mapper was employed to classify the data. In this paper, we extend this method to include other approaches that generate derivative-like and wavelet-based spectral features from the original data. These methods were tested with several supervised classification methods with two Hyperspectral Image (HSI) cubes.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexey Castrodad, Alexey Castrodad, Edward H. Bosch, Edward H. Bosch, Ronald Resmini, Ronald Resmini, } "Hyperspectral imagery transformations using real and imaginary features for improved classification", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65651B (7 May 2007); doi: 10.1117/12.718932; https://doi.org/10.1117/12.718932
PROCEEDINGS
10 PAGES


SHARE
Back to Top