Abstract
The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance and undetermined pixels. However, two key issues remain to be addressed in designing a fuzzy superpixel algorithm for PolSAR image classification. First, the polarimetric scattering information, which is unique in PolSAR images, is not effectively used. Such information can be utilized to generate superpixels more suitable for PolSAR images. Second, the ratio of undetermined pixels is fixed for each image in the existing techniques, ignoring the fact that the difficulty of classifying different objects varies in an image. To address these two issues, we propose a polarimetric scattering information-based adaptive fuzzy superpixel (AFS) algorithm for PolSAR images classification. In AFS, the correlation between pixels’ polarimetric scattering information, for the first time, is considered through fuzzy rough set theory to generate superpixels. This correlation is further used to dynamically and adaptively update the ratio of undetermined pixels. AFS is evaluated extensively against different evaluation metrics and compared with the state-of-the-art superpixels’ algorithms on three PolSAR images. The experimental results demonstrate the superiority of AFS on PolSAR image classification problems.
Highlights
Superpixels algorithms segment an image into smaller regions named superpixels with homogenous appearance and common characteristics [1]
To address the above issues, we propose a polarimetric scattering information based Adaptive Fuzzy Superpixels (AFS) algorithm for polarimetric synthetic aperture radar (PolSAR) image classification
In adaptive fuzzy superpixels (AFS), the correlation between polarimetric scattering information is introduced to cluster pixels, which is measured by a fuzzy equivalence relation
Summary
Superpixels algorithms segment an image into smaller regions named superpixels with homogenous appearance and common characteristics [1]. With the ability to suppress speckle noises of PolSAR image and improve the computation efficiency, superpixels algorithms have been widely investigated in PolSAR classification [18]–[20]. Due to their versatility, superpixels algorithms are often used as a general preprocessing method, applicable to any image applications. To address the above issues, we propose a polarimetric scattering information based Adaptive Fuzzy Superpixels (AFS) algorithm for PolSAR image classification. The unique characteristic of polarimetric scattering information in PolSAR images is introduced to generate improved fuzzy superpixels suitable for PolSAR image. The ratio of undetermined pixels is adaptively adjusted according to the correlation between polarimetric scattering information to produce adaptive fuzzy superpixels.
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