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 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.

Automated summary

Superpixel segmentation is an important technique for image analysis, and a new Fisher vector-based adaptive superpixel segmentation algorithm has been developed to address issues such as low contrast in marine SAR images. This algorithm fuses intensity, spatial, and multiorder features to improve segmentation performance, and adaptive adjustments of feature weights are made to maintain superpixel compactness, demonstrating enhanced segmentation and detection performance in SAR images.