Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection
Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises,...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10012123/ |
| _version_ | 1827362072005967872 |
|---|---|
| author | Lin Bai Cheng Yao Zhen Ye Dongling Xue Xiangyuan Lin Meng Hui |
| author_facet | Lin Bai Cheng Yao Zhen Ye Dongling Xue Xiangyuan Lin Meng Hui |
| author_sort | Lin Bai |
| collection | DOAJ |
| description | Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods. |
| first_indexed | 2024-03-08T07:18:46Z |
| format | Article |
| id | doaj.art-bf55da974f3b4d29bfb6a3982c16b64d |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T07:18:46Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-bf55da974f3b4d29bfb6a3982c16b64d2024-02-03T00:02:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01161042105610.1109/JSTARS.2022.323085910012123Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship DetectionLin Bai0Cheng Yao1https://orcid.org/0000-0002-1560-5526Zhen Ye2https://orcid.org/0000-0001-5410-863XDongling Xue3https://orcid.org/0000-0002-7636-9985Xiangyuan Lin4https://orcid.org/0000-0002-1877-7630Meng Hui5School of Electronics and Control Engineering, Chang'an University, Xi'an, ChinaSchool of Electronics and Control Engineering, Chang'an University, Xi'an, ChinaSchool of Electronics and Control Engineering, Chang'an University, Xi'an, ChinaSchool of Electronics and Control Engineering, Chang'an University, Xi'an, ChinaSchool of Electronics and Control Engineering, Chang'an University, Xi'an, ChinaSchool of Electronics and Control Engineering, Chang'an University, Xi'an, ChinaRecently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods.https://ieeexplore.ieee.org/document/10012123/Deep learningfeature enhancement pyramid (FEP)SAR ship detectionshallow feature reconstruction (SFR)synthetic aperture radar (SAR) |
| spellingShingle | Lin Bai Cheng Yao Zhen Ye Dongling Xue Xiangyuan Lin Meng Hui Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning feature enhancement pyramid (FEP) SAR ship detection shallow feature reconstruction (SFR) synthetic aperture radar (SAR) |
| title | Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection |
| title_full | Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection |
| title_fullStr | Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection |
| title_full_unstemmed | Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection |
| title_short | Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection |
| title_sort | feature enhancement pyramid and shallow feature reconstruction network for sar ship detection |
| topic | Deep learning feature enhancement pyramid (FEP) SAR ship detection shallow feature reconstruction (SFR) synthetic aperture radar (SAR) |
| url | https://ieeexplore.ieee.org/document/10012123/ |
| work_keys_str_mv | AT linbai featureenhancementpyramidandshallowfeaturereconstructionnetworkforsarshipdetection AT chengyao featureenhancementpyramidandshallowfeaturereconstructionnetworkforsarshipdetection AT zhenye featureenhancementpyramidandshallowfeaturereconstructionnetworkforsarshipdetection AT donglingxue featureenhancementpyramidandshallowfeaturereconstructionnetworkforsarshipdetection AT xiangyuanlin featureenhancementpyramidandshallowfeaturereconstructionnetworkforsarshipdetection AT menghui featureenhancementpyramidandshallowfeaturereconstructionnetworkforsarshipdetection |