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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.

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. [ABSTRACT FROM AUTHOR]

In this study, we present an efficient active contour with a joint and region‐scalable distribution metric for interactive natural image segmentation. First, the authors project a red–green–blue image into the CIELab colour space and employ independent component analysis to select two subspace channels. Then, by initialising the evolving curve interactively in terms of a polygonal curve or multiple polygonal curves, they compute a joint probability distribution associated with a region‐scalable mask to model the regional statistics and propose a simple but effective distribution metric to regularise the active contours. Subsequently, they convert the resultant level set function into binary pattern and find the larger 8‐connected regions as the desired objects. Finally, the selected regions are smoothed with a circular averaging filter such that the final segmentation results can be obtained. The proposed approach not only can deal with the complex appearance and intensity in homogeneity, but also has the advantages of fast convergence and easy implementation. The experiments have shown the precise and reliable segmentation results in comparison with the state‐of‐the‐art competing approaches. [ABSTRACT FROM AUTHOR]