SAR Image Object Classification Method Based on Deep Convolutional Neural Network

Abstract

The invention proposes a SAR image target classification method based on a deep convolutional neural network, which is used to improve the classification accuracy of the SAR image target. The implementation steps are: obtain the training sample set and the test sample set containing the SAR target image; remove the background clutter of each SAR image in the training sample set and the test sample set; construct the deep convolution including the transformed sigmoid activation function to form the Enhanced-SE layer Neural network model; train the deep convolutional neural network model; use the trained deep convolutional neural network model to classify the test sample set. The present invention fuses the edge gap of the target area and fills the internal defects of the target area when removing the background clutter in the SAR target image through the morphological closed operation method, effectively retaining the shape characteristics of the target area; forming Enhanced-SE by transforming the sigmoid function layer, inhibiting the automatic extraction of redundant features by the deep convolutional network, and improving the accuracy of SAR image target classification.

Description

technical field [0001] The invention belongs to the technical field of radar, 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 identification, and surveillance 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 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-day, all-weather, long-range, high-resolution, etc., and has played an important role in the fields of reconnaissance, detection and guidance. The image taken by SAR is called SAR image. The high-resolution SAR image can reflect the sca...

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

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