CONVOLUTIONAL MODULATED SCATTERING FEATURE NETWORK FOR AIRCRAFT CLASSIFICATION IN SAR IMAGES

Synthetic aperture radar (SAR) images are widely utilized for the detection and recognition of aircraft targets. Unlike optical images, SAR images possess the advantage of being applicable in all weather conditions and at all times of the day. However, in contrast to optical images, aircraft typically appear as discrete points in SAR images, and their outlines are not distinctly clear. To effectively use the scatter information of aircraft in SAR images, a convolutional modulation scattering feature network (CMSF) is proposed in this paper. Firstly, a scattering feature extraction module is introduced to make full advantage of scattering information. Secondly, following convolution processing, the convolution modulation module is employed to generate a similar fraction matrix. Thirdly, the fusion of scattering features and convolution features is achieved through convolutional modulation and matrix multiplication. Finally, a four-stage convolutional processing is employed to recognize the aircraft target. Extensive experiments conducted on the SAR-Aircraft-1.0 dataset demonstrate the effectiveness of the convolutional modulated scattering feature network for aircraft target classification in SAR images.