Abstract
Abstract. Superpixel segmentation for PolSAR images can heavily decrease the number of primitives for subsequent interpretation while reducing the impact of speckle noise. However, traditional superpixel segmentation methods for PolSAR images only focus on the boundary adherence, the significance of superpixel segmentation will be lost when the accuracy is improved at the expense of computation efficiency. To solve this problem, this paper proposes a novel superpixel segmentation algorithm for PolSAR images based on hexagon initialization and edge refinement. First, the PolSAR image is initialized as hexagonal distribution, where the complexity of searching pixels for relabelling in the local regions can be reduced by 30% theoretically. Second, all pixels in the PolSAR image are initialized as unstable pixels based on the hexagonal superpixels, which can boost the segmentation performance in the heterogeneous regions and effectively maintain all the potential edge pixels. Third, the revised Wishart distance and the spatial distance are integrated as a distance measure to relabel all unstable pixels. Finally, the postprocessing procedure based on a dissimilarity measure is applied to generate the final superpixels. Extensive experiments conducted on both the simulated and real-world PolSAR images demonstrate the superiority and effectiveness of our proposed algorithm in terms of computation efficiency and segmentation accuracy, compared to three other state-of-the-art methods.
Highlights
Polarimetric synthetic aperture radar (PolSAR) is appealing in virtue of containing richer scatter information and the excellent characteristics of all-day and all-weather operation (Debanshu et al, 2018)
In order to balance the segmentation results and computation efficiency, this paper proposes a novel superpixel segmentation algorithm for PolSAR images based on hexagon initialization
To evaluate the performance of different methods for superpixels’ generation, all the experiments on the PolSAR images mentioned above were assessed on four criterions: Boundary Recall (BR), Under-segmentation Error (USE), Achievable Segmentation Accuracy (ASA) and the running time
Summary
Polarimetric synthetic aperture radar (PolSAR) is appealing in virtue of containing richer scatter information and the excellent characteristics of all-day and all-weather operation (Debanshu et al, 2018). Improvements on graph based methods: Liu et al (2013) modified the Ncut algorithm by incorporating the revised Wishart distance and edge map to generate superpixels. In order to balance the segmentation results and computation efficiency, this paper proposes a novel superpixel segmentation algorithm for PolSAR images based on hexagon initialization. Fully considering the particularity of slim and small-size regions for PolSAR images, all of pixels are initialized as unstable points based on hexagons This is beneficial for generating superpixels both in the heterogeneous and homogeneous regions and further boosting the boundary adherence. 2)The experimental results conducted on both the simulated and real-world PolSAR images effectively demonstrate that our algorithm can provide better balanced trade-offs between the computation efficiency and segmentation accuracy, compared to three other competitive state-of-the-art methods The main contributions of our work are summarized as follows: 1) The hexagon initialization is firstly introduced into the superpixel generation for PolSAR images. 2)The experimental results conducted on both the simulated and real-world PolSAR images effectively demonstrate that our algorithm can provide better balanced trade-offs between the computation efficiency and segmentation accuracy, compared to three other competitive state-of-the-art methods
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