SAR image target identification method based on depth increment support vector machine

Abstract

The invention discloses a SAR image target identification method based on a depth increment support vector machine. The method comprises steps of (1), inputting a SAR image; (2) pre-processing the image; (3) constructing an initial training set and an increment training set; (4), initializing the depth increment support vector machine; (5), calculating an initial identification rate of a test sample; (6), updating the depth increment support vector machine; and (7), calculating an identification rate of the test sample. According to the method, advantages of increment learning and depth learning are combined, the depth increment support vector machine is employed to carry out target identification of the SAR image, an increment training sample can be processed, and the depth target information with better discrimination property can be acquired. The method is advantaged in that SAR image target identification precision is improved, and the training time is shortened.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a synthetic aperture radar (Synthetic Aperture Radar, SAR) image target recognition based on a depth incremental support vector machine (Support Vector Machine, SVM) in the technical field of synthetic aperture radar image target recognition method. Aiming at SAR images, the present invention adopts depth increment SVM to carry out target recognition on SAR images, and can be used for target recognition on synthetic aperture radar SAR images. Background technique [0002] Synthetic Aperture Radar (SAR) has the characteristics of all-weather, long-distance, extremely strong penetrating power and high resolution, and is widely used in the national economy and military fields. In the face of the ever-increasing SAR image data collection capabilities, how to accurately and quickly understand and identify these images has attracted more and more attention and attention...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that when the number of training samples changes, all training samples must be recombined, and the computational complexity increases significantly, resulting in long training time, low recognition efficiency, and poor real-time performance.

The disadvantage of this method is that due to the shallow learning method, the learned features cannot represent the original image well, resulting in a low recognition rate.

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