SAR image classification method based on SVM classifier of mixed nucleus function

Abstract

The invention discloses an SAR image classification method for an SVM classifier by using a hybrid kernel function based on wavelet properties, belonging to the technical field of image processing and mainly solving the problem that effectiveness in image characteristic extraction is not sufficient. The method comprises the following steps: firstly, imputing training and testing sample images, and normalizing and marking the sample images; secondly, decomposing the normalized sample images, respectively extracting a plurality of characteristics from various decomposed sub-zones, and storing the characteristics in terms of a structural body of T1 x r; thirdly, according to the characteristics of the sub-zones, constructing the hybrid kernel function based on wavelet properties for the SVM classifier (see the formula on the lower right side, wherein, in the formula, Xi and Xj respectively indicate an ith sample image and a jth sample image, i and j are both less than or equal to 1, xik and xjk respectively indicate the kth chacteristics of the ith sample image and the jth sample, and Rhok is a convex combination coefficient; and fourthly, finishing the classification of the image characteristics by optimizing the convex combination coefficient in the hybrid kernel function. The method has the advantage of high image classification recognition rate and can be used for machine learning and mode identification.

Description

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AI Technical Summary

Benefits of technology

The technical effect of this patented method described in this patents relates to an improved way for learning how well different types of objects look like during imaging scans or other similar situations. This helps identify them more accurately with existing methods such as neural networks trained from scratches made up of small parts called spline points. By creating a mixture-kernel functional model (MF) which combines multiple waveslets into one output signal, it becomes possible to learn about both grayscale and color variations across various scenes without having any specific knowledge at hand.

Problems solved by technology

Technological Problem addressed in this patents relates to improving the performance of sidelaryadioscopy systems on sea water. Current techniques involve manually selecting specific data points based upon previous observations without any consideration regarding how other factors affect them during analysis. There is a technical solution proposed specifically aimed towards automatic identification of objects within range scanning ranges.

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