Simultaneous Spectral/Spatial Detection of Edges for Hyperspectral Imagery: The HySPADE Algorithm RevisitedApril 2012
Topics: Image Processing, Sensor Technology, Signal Processing
The hyperspectral/spatial detection of edges (HySPADE) algorithm, originally published in 2004 , has been modified and applied to a wider diversity of hyperspectral imagery (HSI) data. As originally described in , HySPADE operates by converting the naturally two-dimensional edge detection process based on traditional image analysis methods into a series of one-dimensional edge detections based on spectral angle. The HySPADE algorithm: i) utilizes spectral signature information to identify edges; ii) requires only the spectral information of the HSI scene data and does not require a spectral library nor spectral matching against a library; iii) facilitates simultaneous use of all spectral information; iv) does not require endmember or training data selection; v) generates multiple, independent data points for statistical analysis of detected edges; vi) is robust in the presence of noise; and vii) may be applied to radiance, reflectance, and emissivity data—though it is applied to radiance and reflectance spectra (and their principal components transformation) in this report. HySPADE has recently been modified to use Euclidean distance values as an alternative to spectral angle. It has also been modified to use an N-pixel x N-pixel sliding window in contrast to the 2004 version which operated only on spatial subset image chips. HySPADE results are compared to those obtained using traditional (Roberts and Sobel) edge-detection methods. Spectral angle and Euclidean distance HySPADE results are superior to those obtained using the traditional edge detection methods; the best results are obtained by applying HySPADE to the higher-order, information-containing bands of principal components transformed data (both radiance and reflectance). However, in practice, both the Euclidean distance and spectral angle versions of HySPADE should be applied and their results compared. HySPADE results are shown; extensions of the HySPADE concept are discussed as are applications for HySPADE in HSI analysis and exploitation.