A Min-Pooling Detection Method for Ship Targets in Noisy SAR Images

Abstract

As the application of deep learning in general optical images becomes more and more widespread, the field of remote sensing images also begins to pay attention to the application of deep learning methods. Although deep learning detection algorithms have achieved better results than traditional detection algorithms, the detection results for poorly imaged SAR images still need improvement, and processing poorly imaged noisy SAR images is still a big challenge for existing algorithms. To address the problem of low precision and recall of existing algorithms for noisy SAR image detection, we propose a convolutional neural network detection algorithm based on min-pooling. First, we design a feature processing layer with min-pooling as the main structure to suppress the noise and then use a feature fusion layer to compensate for the missing information caused by pooling. To avoid problems such as redundancy in computation caused by the anchor-base algorithm, we choose the anchor-free algorithm as the main structure of ship detection. Finally, the model is evaluated using ordinary SAR image datasets and noisy SAR image datasets. Experiment results show that our proposed method has a better detection effect for noisy SAR images than other object detection models.