SDBANDS Band feature extraction for spectral data Define bands manually: P=SDBANDS(DATA,'bands',{30:40 50:80}) Sequential band definition by band size and step: P=SDBANDS(DATA,'size',10,'step',10) Cluster spectral domain with k-means: P=SDBANDS(DATA,'cluster',10) Display info on bands SDBANDS(P) INPUTS DATA Data set with spectral walevengths (features express continuity) OUTPUTS P Band feature extractor OPTIONS 'bands',DEF Define bands manually. DEF is a continuous vector of wavelength indices or a cell array of such vectors 'size',S Define band size in number of wavelengths for sequential definition 'step',ST Define step between sequentially defined bands 'cluster',N Cluster spectral domain into N clusters with k-means 'mean' Default feature extraction method (band is a mean of wavelengths) 'LDA' Feature extraction: Fisher projection per band (see SDLDA) 'no display' Do not show any output DESCRIPTION SDBANDS creates band feature extractor for spectral measurements. By wavelength we denote individual feature of DATA, assuming that there is continuity (neighboring relationship) between the features. A band is a continuous set of wavelengths. Band extractor is defined in two steps, namely band definition and band feature extraction. Bands may be defined manually with 'bands' option, sequentially with 'size' and 'step' or by clustering the spectral domain with 'cluster'. Clustering algorithm defines similar groups of wavelengths in the spectral domain. Note, that spectral clusters may contain spectrally-disjoined wavelengths and, therefore, yield more spectral bands than the number of clusters. Band feature extractor is either mean of wavelengths or LDA projection maximizing class separability for each band. SDBANDS also displays information on trained bands. SEE ALSO SDLDA, SDKMEANS