Abstract
Segmentation of high noise imagery like Synthetic Aperture Radar (SAR) images is still one of the most challenging tasks in image processing. While level set, a novel approach based on the analysis of the motion of an interface, can be used to address this challenge, the cell-based iterations may make the process of image segmentation remarkably slow, especially for large-size images. For this reason fast level set algorithms such as narrow band and fast marching have been attempted. Built upon these, this paper presents an improved fast level set method for SAR ocean image segmentation. This competent method is dependent on both the intensity driven speed and curvature flow that result in a stable and smooth boundary. Notably, it is optimized to track moving interfaces for keeping up with the point-wise boundary propagation using a single list and a method of fast up-wind scheme iteration. The list facilitates efficient insertion and deletion of pixels on the propagation front. Meanwhile, the local up-wind scheme is used to update the motion of the curvature front instead of solving partial differential equations. Experiments have been carried out on extraction of surface slick features from ERS-2 SAR images to substantiate the efficacy of the proposed fast level set method.
Highlights
From the time of its inception, Synthetic Aperture Radar (SAR) imaging systems have provided a remote sensing resource complementary to optical and thermal-infrared sensors
In accordance with the procedure described in the previous section, the SAR image was segmented using the proposed level set method
The fast marching and the proposed fast level set methods deal with the pixels close to the slick propagation boundary in a similar way, the proposed method is more efficient than the fast marching method in both the images (Figures 5 and 6) with the single initial seed for the first image (Figure 5) and multiple seeds for the second image (Figure 6)
Summary
From the time of its inception, Synthetic Aperture Radar (SAR) imaging systems have provided a remote sensing resource complementary to optical and thermal-infrared sensors. Level set was proposed by Osher and Sethian [8] as an interface propagation technique for various applications including image segmentation In this method, a curve is embedded as a zero level set of a higher dimensional surface. In the traditional level set technique, instead of tracking the points on the interface itself, the interface is embedded as the zero level set propagates by iterations For this reason the standard level set algorithm could possibly be rather slow for real-time or near real-time image processing. Based upon our previous work using a level set for image segmentation [7], this paper proposes a more efficient, three-component based level set algorithm for noisy SAR image segmentation It operates in a grid domain, which updates the adjoining pixels around the zero level set, and only the boundary and neighboring pixels in the propagation direction are considered in the computation process. In addition the proposed techniques were evaluated against other level set methods such as fast marching, and the results confirmed the efficiency gains of the proposed method
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