SAR image target classification method based on deep convolutional neural network

Abstract

The invention provides an SAR image target classification method based on a deep convolutional neural network. The SAR image target classification method is used for improving SAR image target classification precision. The method comprises the following implementation steps: obtaining a training sample set and a test sample set which comprise SAR target images; removing background clutters of eachSAR image in the training sample set and the test sample set; constructing a deep convolutional neural network model containing an Enhanced-SE layer transformed by a sigmoid activation function to form; training the deep convolutional neural network model; and classifying the test sample set by using the trained deep convolutional neural network model. According to the method, when background clutters in the SAR target image are removed through the morphological closed operation method, the edge gap of the target area is fused, the internal defect of the target area is filled, and the shape features of the target area are effectively reserved; Athe Enhanced-SE layer is formed by modifying the sigmoid function, the deep convolutional network is inhibited from automatically extracting redundant features, and the SAR image target classification precision is improved.

Description

Technical field [0001] The invention belongs to the field of radar technology, and relates to a SAR image target classification method, in particular to a SAR image target classification method based on a deep convolutional neural network, which can be used for target detection, target recognition, and surveillance monitoring of SAR images. Background technique [0002] Image object classification is an image processing method that distinguishes different types of objects according to their different characteristics reflected in the image information. Image object classification can classify image objects based on image features such as color, texture, shape, and spatial relationship. [0003] Synthetic Aperture Radar SAR has the characteristics of all-weather, all-weather, long range, high resolution, etc., and has played an important role in the fields of reconnaissance, detection and guidance. The images taken by SAR are called SAR images. High-resolution SAR images can reflect...

AI Technical Summary

Problems solved by technology

The invention adds a deformable convolutional network to the deep convolutional network, and the convolution kernel with an offset can be sampled arbitrarily near the sampling point, which solves the problem that the SAR image target feature position is not stable in the SAR image target classification method. The problem of low classification accuracy caused by accurate target classification, but because this method only uses simple operations such as median filtering to remove the artificially spliced ​​background clutter, it has not effectively removed the background clutter in the target image of the SAR image and suppressed the deep convolution. The neural network automatically extracts redundant features, so the accuracy of SAR image target classification is still low

Images