Adaptive Superpixel Segmentation of Marine SAR Images by Aggregating Fisher Vectors

Superpixel segmentation is an important technique for image analysis. In this article, we develop a new superpixel segmentation approach and investigate its application on ship target detection in marine synthetic aperture radar (SAR) images. Existing superpixel segmentation algorithms often simply...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.2058-2069
Main Authors: Wang, Xueqian, He, You, Li, Gang, Plaza, Antonio
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Clutter
Detection
Detectors
Feature extraction
Fisher vectors (FVs)
Image analysis
Image edge detection
Image processing
Image segmentation
Marine vehicles
Radar imaging
Radar polarimetry
SAR (radar)
ship detection
Ships
superpixel segmentation
Surface clutter
Synthetic aperture radar
synthetic aperture radar (SAR)
Target detection
Vectors
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Summary:Superpixel segmentation is an important technique for image analysis. In this article, we develop a new superpixel segmentation approach and investigate its application on ship target detection in marine synthetic aperture radar (SAR) images. Existing superpixel segmentation algorithms often simply consider the intensity and spatial features, which may degrade the segmentation performance due to the low contrast between ship targets and the sea clutter background in marine SAR images. Besides, it is difficult for existing algorithms to adaptively select the weights of the features. Here, we propose a new Fisher vector (FV)-based adaptive superpixel segmentation (FVASS) algorithm to address the aforementioned issues. Our newly developed FVASS not only fuses the intensity and spatial features, but also the multiorder features introduced by FVs, resulting in a better segmentation performance (even with low signal-to-clutter ratios). The weights of the features considered in FVASS are adaptively adjusted by minimizing the sum of within-superpixel variances to maintain the compactness of superpixels. Experiments demonstrate that, compared with commonly used superpixel segmentation methods, the proposed FVASS algorithm enhances the segmentation performance of SAR images and further improves the detection performance of existing superpixel-based ship detectors.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2021.3051301