Ship Segmentation via Encoder-Decoder Network With Global Attention in High-Resolution SAR Images

Ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices' coordinates of the bounding box covering the...

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Vydané v:IEEE geoscience and remote sensing letters Ročník 19; s. 1 - 5
Hlavní autori: Li, Jichao, Gou, Shuiping, Li, Ruimin, Chen, Jia-Wei, Sun, Xiaolong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Apexes
Coders
Convolution
Detection
Encoder–decoder structure
Feature extraction
global attention module
high-resolution synthetic aperture radar (SAR) images
Image processing
Image resolution
Image segmentation
Marine vehicles
Methods
Modules
Neural networks
Object recognition
Performance evaluation
Radar imaging
Radar polarimetry
SAR (radar)
Semantics
Shape
ship segmentation
Synthetic aperture radar
Target recognition
Wavelet transforms
ISSN:1545-598X, 1558-0571
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Popis
Shrnutí:Ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices' coordinates of the bounding box covering the ship targets but cannot provide more detailed contour information. Target segmentation can further explore the shape and edge information of the objects, which can be used as a blazing novel means for automatic object detection. In this letter, a 3-D atrous encoder-decoder neural network with global attention modules (GAM-EDNet) is proposed to achieve ship segmentation in SAR images. The encoder-decoder structure with atrous convolution is developed as the network body to fully exploit the structural information of the ship targets with various sizes. To increase the structural information of the single-polarization SAR images, a 3-D image cube is designed as the input of the GAM-EDNet. A global attention module is proposed to further improve the segmentation performance by integrating the high-level semantic features with the low-level location features. Besides, an SAR ship segmentation dataset (SAR-HR4) is built to evaluate the segmentation performance, and the experimental results show that the proposed GAM-EDNet achieves better performance than other state-of-the-art methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3100572