Polarization SAR image classification method based on object orienting and spectral clustering

Abstract

The invention discloses a polarization SAR image classification method based on object orienting and spectral clustering. The method mainly solves the problem that the accuracy rate of image classification of a polarization synthetic aperture radar SAR in the prior art is low. The method comprises the implementation steps that (1), filtering is carried out on coherence matrixes of polarization SAR data, and the coherence matrixes are used for synthesizing a color image of the polarization synthetic aperture radar SAR; (2), related parameters of the polarization synthetic aperture radar SAR are set; (3), by means of combination with the related parameters of the polarization synthetic aperture radar SAR, all pixels of the color image of the polarization synthetic aperture radar SAR are combined to form super-pixel blocks; (4), all the super-pixel blocks of the color image of the polarization synthetic aperture radar SAR are combined; (5), class centers of the combined super-pixel blocks are calculated; (6), spectral clustering is carried out on the class centers of the super-pixel blocks to finish final classification. According to the method, the influence of noise is overcome, the accuracy rate of classification is increased, and the method can be applied to terrain classification and target recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an object-oriented and spectral clustering-based polarization synthetic aperture radar SAR image classification method in the technical field of synthetic aperture radar, which can be used for forest fire monitoring, vegetation coverage, marine pollution etc. Background technique [0002] With the polarization synthetic aperture radar SAR more and more attention. There are endless methods for classifying polarimetric SAR data. According to whether manual guidance is required, it can be divided into supervised and unsupervised; according to the different algorithms used, it can be divided into statistics, knowledge, neural network, fuzzy statistics, wavelet, support vector machine and fractal, etc.; according to whether it needs Spatial information can be divided into area-based and pixel-based; according to the utilization of polarization information, it can be d...

AI Technical Summary

Problems solved by technology

This method is simple and fast, but still has the disadvantages that the number of classification categories is fixed, and because only the scattering features are considered, it leads to many misclassification points, low classification accuracy, poor anti-noise ability, and poor regional consistency.

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