Research On SAR Image Target Recognition Method Based On Deep Learning

Synthetic aperture radar(SAR)has the capability to capture high-resolution remote sensing data in day-and-night,all-weather,thus having become an important detection tool in military application scenarios.However,the rich feature information of SAR images will become difficult to obtain due to various factors,such as sensitivity to target location,noise corruption,and insufficient samples.The key to SAR image interpretation is to extract useful SAR image feature information from various complex scenes to accomplish specific targeted tasks.Automatic target recognition(ATR)technology plays a vital role in SAR image interpretation.With the intensive research of deep learning theory,ATR technology has been further developed in practical applications.A standard SAR ATR system mainly includes three stages: detection,discrimination and classification,in which classification is an important research stage in ATR technology.In order to enhance the classification performance,feature extraction and classifier selection have become hot and difficult points in SAR image target recognition.From the perspective of target feature extraction,this thesis mainly studies the deep learning-based SAR image target recognition methods for a variety of extended scenes,speckle noise interference scenes and few-shot scenes.In the meanwhile,relevant experiments are carried out to evaluate the performance of the models.The main research work and achievements are as follows:(1)Aiming at the problem of SAR image target recognition in various extended scenes,a SAR image target recognition method based on dynamic perception attention network is proposed.The network improves the feature representation ability of the convolutional neural network-based model through the dynamic perception convolution layer and spatial and channel-wise attention mechanism without increasing the depth and width of the network,so as to realize robust target feature extraction in a variety of extended scenarios.(2)With regard to the problem of SAR image target recognition under speckle noise corruption,a deep attention SAR image target recognition method assisted by despeckling task is proposed.The network is a despeckling and classification cascaded multi-scale residual attention network.In the despeckling sub-network,the multi-scale feature extraction is realized by using dilated convolution,and the adaptive feature channel selection is performed by the attention mechanism,thereby learning the noise information effectively.In order to extract more discriminative target features,a cross-dimensional interactive attention mechanism is embedded in the classification sub-network to further improve the target recognition performance under speckle noise corruption.(3)Considering the problem of SAR image target recognition under the condition of a few samples,a few-shot SAR image target recognition method based on multi-task representation learning is proposed.On the one hand,the network aims to have the ability to perceive the input transformation,identity itself and class discrimination simultaneously.On the other hand,the network can also extract the morphological features of the target and achieve feature refinement through the channel attention mechanism.The powerful feature learning ability of the network provides a guarantee for feature extraction under the condition of small samples.