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SUPERPIXEL SEGMENTATION FOR POLARIMETRIC SAR IMAGERY USING
LOCAL ITERATIVE CLUSTERING
ABSTRACT
The simple linear iterative clustering (SLIC) algorithmshows good performance in
superpixel generation for opticalimagery. However, SLIC can perform poorly when thereis too
much noise in the image. To solve this problem, we haveimproved the cluster center
initialization step and the postprocessingstep, and then introduce the SLIC super pixel
segmentation algorithm to the polarimetric synthetic aperture radar (PolSAR)image processing
field. Experiments using AirSAR and ESARL-band PolSAR data show that the improved SLIC
algorithm canovercome the effect of speckle noise in PolSAR imagery, and its how a better
performance in detail preservation than the originalSLIC algorithm and the normalized cuts
superpixel segmentationalgorithm.

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Superpixel segmentation for polarimetric sar imagery using local iterative clustering

  • 1. SUPERPIXEL SEGMENTATION FOR POLARIMETRIC SAR IMAGERY USING LOCAL ITERATIVE CLUSTERING ABSTRACT The simple linear iterative clustering (SLIC) algorithmshows good performance in superpixel generation for opticalimagery. However, SLIC can perform poorly when thereis too much noise in the image. To solve this problem, we haveimproved the cluster center initialization step and the postprocessingstep, and then introduce the SLIC super pixel segmentation algorithm to the polarimetric synthetic aperture radar (PolSAR)image processing field. Experiments using AirSAR and ESARL-band PolSAR data show that the improved SLIC algorithm canovercome the effect of speckle noise in PolSAR imagery, and its how a better performance in detail preservation than the originalSLIC algorithm and the normalized cuts superpixel segmentationalgorithm.