Abstract
Image segmentation has gained increasing popularity and has been widely applied in various image processing tasks recently. A simple linear iterative clustering (SLIC) superpixel generating algorithm for optical images based on k-means clustering approach works well in terms of computational simplicity and segmentation speed. However, due to the inherent speckle noise, it may generate poor segmentations for synthetic aperture radar (SAR) images. In this paper, an improved similarity measure combining pixel intensity and location similarity with edge information is utilised to replace the Euclidean distance in CIELAB colour space for performing local clustering to generate superpixels by using SLIC. Additionally, a constructed image is used to perform the superpixel cosegmentation, simultaneously segmenting the SAR image pairs. The generated superpixels can be taken as basic units for the subsequent change detection. The experimental results conducted on one simulated dataset and one real-world SAR dataset demonstrate the feasibility and effectiveness of the proposed algorithm.
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