초록 ▼

A method for remote scenes classification comprising the steps of (a) preparing a reference template for classification of the remote scenes via (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a f

A method for remote scenes classification comprising the steps of (a) preparing a reference template for classification of the remote scenes via (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of the preclassified reference scenes; (iii) employing a principal component analysis for extracting the spectral cube for decorrelated spectral data characterizing the reference scenes; and (vi) using at least a part of the decorrelated spectral data for the preparation of the reference template for remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of analyzed remote scenes, such that a spectrum of each pixel in the remote scenes is obtained; (c) employing a decorrelation statistical method for extracting decorrelated spectral data characterizing the pixels; and (d) comparing at least a part of the decorrelated spectral data extracted from the pixels of the remote scenes with the reference template.

대표청구항 ▼

[ What is claimed is:] [17.] A method for remote scenes classification comprising the steps of:(a) preparing a reference template for classification of remote scenes via:(i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified referen

[ What is claimed is:] [17.] A method for remote scenes classification comprising the steps of:(a) preparing a reference template for classification of remote scenes via:(i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes;(ii) using a first spectral imager for measuring a spectral cube of said preclassified reference scenes;(iii) employing a principal component analysis for extracting said spectral cube for decorrelated spectral data characterizing said reference scenes via:(a) selecting k spectral slices for said spectral cube of said reference scenes;(b) calculating an average spectrum for each of said reference scenes;(c) stretching said average spectra for obtaining a stretched average spectrum for each of said reference scenes;(d) averaging said stretched average spectra for each of said reference scenes for obtaining an ensemble average spectrum for each of said reference scenes;(e) calculating a k dimension eigen system for said ensemble average spectra and extracting N eigenvectors;(f) using said N eigenvectors for defining an N-dimension vector for each of said reference scenes; and(g) using said N-dimension vectors for preparing said reference template for the remote scenes classification;(b) using a second spectral imager for measuring a spectral cube of the remote scenes, such that a spectrum of each pixel in the remote scenes is obtained;(c) projecting said spectrum of each of said pixels into said N eigenvectors for obtaining a projected N dimension vector for each of said pixels; and(d) correlating each of said projected N dimension vectors with said reference template.