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Fast Superpixel Segmentation Algorithm for PolSAR Images
doi: 10.12000/JR17018
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Abstract
As a pre-processing technique, superpixel segmentation algorithms should be of high computational efficiency, accurate boundary adherence and regular shape in homogeneous regions. A fast superpixel segmentation algorithm based on Iterative Edge Refinement (IER) has shown to be applicable on optical images. However, it is difficult to obtain the ideal results when IER is applied directly to PolSAR images due to the speckle noise and small or slim regions in PolSAR images. To address these problems, in this study, the unstable pixel set is initialized as all the pixels in the PolSAR image instead of the initial grid edge pixels. In the local relabeling of the unstable pixels, the fast revised Wishart distance is utilized instead of the Euclidean distance in CIELAB color space. Then, a post-processing procedure based on dissimilarity measure is empolyed to remove isolated small superpixels as well as to retain the strong point targets. Finally, extensive experiments based on a simulated image and a real-world PolSAR image from Airborne Synthetic Aperture Radar (AirSAR) are conducted, showing that the proposed algorithm, compared with three state-of-the-art methods, performs better in terms of several commonly used evaluation criteria with high computational efficiency, accurate boundary adherence, and homogeneous regularity. -
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References
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- Figure 1. The sketch map of initialization of two methods. Ci indicates the ith cluster center, and S is the initial grid width. The pixels filled with black are the initial cluster centers. The initial unstable pixels of the IER algorithm are the yellow pixels, while the initial unstable pixels of the proposed method are the yellow, white, and black pixels.
- Figure 2. Pauli_RGB image of the simulated image
- Figure 5. Generated superpixels of the four competitive methods. The second row denotes the final superpixel maps of different methods. The red lines superimposed onto the Pauli-RGB images depict the superpixel boundaries. The third row gives the representation maps, where the color of each pixel is replaced by the average value of the superpixel to which this pixel belongs
- Figure 3. Comparison of two methods of initialization based on the simulated PolSAR images
- Figure 4. The results of four algorithms
- Figure 6. Three PolSAR image patches (first column) and corresponding superpixels provided by the standard SLIC (second column), IER (third column), SLIC-GC (fourth column), and the proposed method (fifth column)