Abstract
Abstract. To effectively describe the uncertainty of remote sensing image segmentation, a novel region-based algorithm using fuzzy clustering and Kullback-Leibler (KL) distance is proposed. By regular tessellation, the image domain is completely divided into several sub-blocks to overcome the complex noise existed in high-resolution remote sensing images. Taking the blocks as the basic processing units, KL divergence is used to model the distance between blocks and clusters, which enables the model to describe the uncertainty of the non-similarity relationship. Besides, based on the theory of Markov Random Field (MRF), the regionalized KL entropy regularization term is established and added to the objective function to further consider the spatial constraints. Finally, the optimal segmentation results are obtained by estimating the parameters. The experiments carried out on different kinds of remote sensing images by comparing algorithms fully demonstrate the performance of the proposed algorithm.
Highlights
Image segmentation is the key step of image processing, the segmentation accuracy can directly affect the quality of image interpretation (Dass et al, 2012; Wang et al, 2018)
Fuzzy set is one of the most effective tools to deal with uncertainty problems, where Fuzzy C-Means (FCM) is the most classical algorithm in image segmentation (Gong et al, 2013; Memon, Lee, 2018)
Miyamoto and Mukaidono (1997) proposed an Entropy-based FCM algorithm (EFCM). It defined an entropy regularization term based on fuzzy membership according to maximum entropy theory
Summary
Image segmentation is the key step of image processing, the segmentation accuracy can directly affect the quality of image interpretation (Dass et al, 2012; Wang et al, 2018). With the increase of spatial resolution, the rich and detailed surface information increases the heterogeneity of homogeneous regions and complicates the spatial correlation of spectra (Yuan et al, 2014) All of these characteristics bring more uncertainty to image segmentation and make high accuracy segmentation face new challenges (Heshmati et al, 2016). Miyamoto and Mukaidono (1997) proposed an Entropy-based FCM algorithm (EFCM) It defined an entropy regularization term based on fuzzy membership according to maximum entropy theory. All of these algorithms mentioned above are pixel-based, they can not effectively overcome the complex noise existed in high-resolution remote sensing image. The region-based fuzzy clustering image segmentation algorithm with KL distance is proposed to increase the ability to overcome noise and describe the segmentation uncertainty. For estimating the optimal parameter, the Lagrange function method and Markov Chain Monte Carlo (MCMC) method (zhao et al, 2014) are selected according to the characteristics of segmentation model parameters
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