Polarized SAR image classification method based on K mean value and depth SVM

Abstract

The invention discloses a polarized SAR image classification method based on a K mean value and a depth SVM. The polarized SAR image classification method mainly solves the problems of low classification accuracy and classification efficiency of an existing polarized synthetic aperture radar (SAR) classification method. The polarized SAR image classification method comprises the steps of (1) inputting images; (2) performing filtering; (3) extracting features; (4) establishing a misclassification set; (5) establishing a nearest neighbor sample set; (6) establishing a final training set; (7) establishing a depth support vector machine classifier; (8) performing classification; (9) calculating accuracy. By means of the polarized SAR image classification method, polarized SAR images can be classified accurately, the classification time of the polarized SAR images can be shortened effectively, and target recognition and tracking of the polarized SAR images are achieved.

Description

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AI Technical Summary

Benefits of technology

The technical effect of this patented technology described in this patents are improved efficiency when selecting relevant data for analysis compared to previous methods while reducing errors associated with other techniques such as wavelets or spectral decomposition algorithms. Additionally, combining these two types of processing together results in an improvement on both performance and speedup during the process.

Problems solved by technology

Technics discuss how polariscope imagery works well when analyzed under specific conditions such as wavelengths or frequency bands. These techniques involve combining scattered light patterns together through various means like wavelets transforms, spectral analysis, pattern recognition algorithms, and learning models. They aim to improve upon existing systems while reducing their computational load.

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