The invention discloses a super-pixel
polarimetric SAR land feature classification method based on sparse representation. The method comprises: inputting
polarimetric SAR image data to be classified,
processing the image, and thereby obtaining a pseudocolor image corresponding to Pauli
decomposition; performing super-
pixel image over-segmentation on the pseudocolor image to obtain a plurality of super-pixels; extracting features, which are seven-dimensional, of
radiation mechanism of the original
polarimetric SAR image as features of every pixel; performing super-pixel united sparse representation to obtain sparse representation of each super-pixel feature; classifying by using a
sparse representation classifier; working out the mean value of each super-pixel
covariance matrix, then performing super-pixel complex Wishart iteration by using the classifying result in the last step, and at last obtaining a final classifying result. According to the super-pixel polarimetric SAR land feature classification method based on sparse representation, the problem that traditional classifying areas based on the
single pixel are poor in consistency is solved, and
operating speed of the
algorithm is greatly increased on basis of improving accelerate.