Polarized SAR image classifying method based on sparse automatic encoder

Abstract

The invention discloses a polarized SAR image classifying method based on a sparse automatic encoder. The problem that extraction of polarized SAR image features is influenced by data irrelevance and redundancy, accordingly the classifying process is complicated and unreasionabl feature selection causes low classifying accuracy is mainly solved. The polarized SAR image classifying method based on the sparse automatic encoder comprises the specific steps of 1 inputting coherence matrixes of polarized SAR images to be classified; 2 perform filtering, 3 selecting samples; 4 obtaining sparse principal components; 5 training the sparse automatic encoder; 6 extracting the features; 7 performing classification through a support vector machine; 8 outputting a classification result. The polarized SAR image classifying method has the advantage of having remarkable polarized SAR image classifying effect and can be further used for target detection and target recognition of the polarized SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarization synthetic aperture radar (Synthetic Aperture Radar SAR) image classification method based on a sparse automatic encoder in the field of target recognition. The invention can be used for ground object classification and target recognition of polarization synthetic aperture radar SAR images. Background technique [0002] Compared with traditional synthetic aperture radar, polarimetric synthetic aperture radar (SAR) uses the scattering information of multiple channels to obtain a more comprehensive understanding of the target. The classification of polarimetric SAR SAR images is an important research content of polarimetric SAR SAR image interpretation. The classification map can be used as an intermediate result to provide auxiliary information for edge extraction, target detection, recognition, etc., and can also be directly output as the final result...

AI Technical Summary

Problems solved by technology

Although this method can cluster on a sample space of any shape and converge to the global optimal solution, it still has the disadvantage that when using a Gaussian function to construct a similarity matrix, the scale parameter has a great influence on the classification structure, and it is difficult to obtain the optimal solution. parameters, leading to unreasonable feature extraction, affecting the stability of image segmentation, resulting in a decrease in classification accuracy

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