SAR Image Target Classification Algorithm Based on Integrated Improved Convolutional Block Attention Module
In the Synthetic Aperture Radar(SAR)images,the contours and details of targets are often complex.Tradi-tional Convolutional Neural Network(CNN)only uses a single mean parameter for indiscriminate feature extraction,so it cannot distinguish the differences between SAR features well.To address this issue,a SAR image target classification al-gorithm is proposed based on the Integrated Improved Convolutional Block Attention Module(ICBAM),as ICBAM_CNN.Firstly,this module designs an improved CBAM attention mechanism by introducing variance parameters into the traditional CBAM module,which helps the classification and recognition network better learn the differential information between the convolutional layer output and channel attention of different targets in SAR images,and improves the separa-bility of different SAR target features.In addition,ICBAM has designed a center coordinate attention mechanism to better capture the center distribution features of targets in SAR images,effectively suppressing clutter interference on SAR tar-get classification images.Finally,in order to improve efficiency,the improved ICBAM module is integrated into the CNN network to achieve SAR image target classification.ICBAM_CNN deeply integrates multi-level features of SAR image targets and improves the separability of SAR target features,enabling high-precision and efficient recognition and classifi-cation of SAR image targets.Experiments are conducted on the MSTAR dataset,and the results showed that compared to traditional CBAM methods,the improved ICBAM method improved precision by 2.44%,recall by 2.24%,and F1-score by 2.34%.
SAR image target classificationImproved Convolutional Block Attention Module(ICBAM)Integrated CNN network with ICBAM(ICBAM_CNN)central coordinate attention mechanismmulti-level feature fusion
万方数据
维普
