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

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 detec...

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Bibliographic Details
Published inIEEE access Vol. 11; p. 1
Main Authors Li, Xiangwen, Zhou, Ling, Wu, Haoyu, Yang, Bin, Zhang, Wenjing, Gu, Jiewen, Gan, Yinlu
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial neural networks
Datasets
Deep learning
Feature extraction
Image detection
Machine learning
Marine vehicles
min-pooling
neural networks
Noise measurement
Object detection
Object recognition
Radar polarimetry
Redundancy
Remote sensing
Sea surface
ship detection
Synthetic aperture radar
Target detection
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3262804