Super-pixel polarimetric SAR land feature classification method based on sparse representation

Abstract

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.

Description

Technical field: [0001] The invention belongs to the field of image processing and remote sensing technology, and relates to the classification of polarimetric SAR images, in particular to a polarization SAR classification method based on sparse representation and superpixels, which can be used to classify polarimetric SAR with regional consistency Images are classified. Background technique: [0002] Polarimetric Synthetic Aperture Radar (POLSAR) image processing is a key subject in national defense construction. Compared with ordinary single-polarization SAR, polarimetric SAR uses scattering matrix or coherence matrix and covariance matrix to record ground feature information. Due to the different physical characteristics of different targets, the amplitude, phase, polarization ratio, and scattering entropy are all different in different polarization states. Therefore, polarimetric SAR can obtain more abundant information on ground objects. Polarimetric SAR image classif...

AI Technical Summary

Problems solved by technology

[0006] The above methods make good use of polarization information for classification, but these pixel-based classification methods do not take advantage of the spatial similarity of polarimetric SAR objects, that is, adjacent objects are also very close in category

Therefore, the regional consistency of the classification results is often poor

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