An SAR Target Detector Based on Gradient Harmonized Mechanism and Attention Mechanism

Abstract

In this letter, a target detector based on gradient harmonized mechanism (GHM) and attention mechanism is proposed to realize synthetic aperture radar (SAR) target detection in complex scenes. Considering the imbalance of positive and negative examples in SAR target detection, we use RefineDet as our backbone network. RefineDet can mitigate this imbalance problem by introducing the idea of two-step classification and regression into the one-stage detector. However, RefineDet only selects a part of examples for training and does not make full use of the information of all examples. Therefore, we apply GHM to the classification loss function of RefineDet, so that the network can make full use of all examples and increase the weights of hard examples adaptively in the loss function to reduce the false alarms and the missing alarms. In addition, to achieve a better detection performance in SAR images with complex scenes, a multiscale feature attention module (MFAM) is embedded into the network. By applying channel and spatial attention mechanisms to the multiscale feature maps, the MFAM can highlight the significant information and suppress the interference caused by clutter. The extensive experimental results based on the measured SAR dataset verify the effectiveness of the proposed method.