An extraction method for a spectral feature cross-correlation vector in a hyperspectral image classification, comprising hyperspectral image data preprocessing──normalization, denoising, and dimension reduction; boostrap sampling and weighted averaging to acquire a reference sample set; spectrum signal random process hypotheses──hypothesis one, spectrum signals are random trials at any one moment of a stationary random process, hypothesis two, probabilities are equal for every random trial value, then, the spectrum signals are abstracted on the basis of a random process autocorrelation theory to arrive at an autocorrelation coefficient calculation formula, and then finally combined into a autocorrelation feature vector; and, a method of optimal directions (MOD) is employed for sparse decomposition of the correlation feature vector. The present invention sets forth the feature extraction method in hyperspectral classification from a random process correlation perspective, thus providing improved noise immunity and high stability, and allowing increased precision for hyperspectral classification.
EXTRACTION METHOD FOR SPECTRAL FEATURE CROSS-CORRELATION VECTOR IN HYPERSPECTRAL IMAGE CLASSIFICATION