Aircraft Detection and Classification Based on Joint Probability Detector Integrated With Scattering Attention.

Synthetic aperture radar (SAR) image intelligence interpretation is always a challenging task due to its special imaging mechanism. This paper focuses on the aircraft detection and recognition in large-scale SAR images. The objects in large-size SAR images are usually sparsely distributed and tend to be highly concentrated in certain areas. Therefore, the detection and classification in SAR images are affected easily by imaging environment, leading to ineffective and instability. It suffers from time-consuming, high false-alarm rate, and low recognition rate. To alleviate these problems, a joint probability detector (JPD) integrated with scattering attention (SA) is designed for aircraft detection and classification. Based on the fact that all aircraft are parked in the airport area, an airport detection method with mixed strategy (ADMS) is proposed to obtain the valid region (airport), narrowing downing the scope of detection area, increasing the detection efficiency as well as suppressing a lot of false alarms outside the airport. Considering that it's easy to generate false alarms within the airport on account of clutter scattering interference, a two-stage detector is introduced to improve the accuracy by replacing a stronger region proposal network (RPN) and optimizing a lower bound to a joint probabilistic objective over two stages. Furthermore, the classification encoder module incorporates scattering attention into convolution neural network to leverage the distribution relation among strong scattered points to enhance the classification performance. Finally, extensive experiments on the ZKXT and GaoFen-3 datasets verify the effectiveness and superiority of the proposed approach, and surpass runner up by 5.3% with mF1 indicator. On a large GaoFen3 image (16550× 11945 pixels), the proposed method improves the detection speed by 187.52%, without sacrificing the accuracy. More importantly, the proposed method has been applied to the SAR image interpretation and achieved the expected effect in the actual application.