A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution

Zou, Huanxin; Qin, Xianxiang; Zhou, Shilin; Ji, Kefeng

doi:10.3390/s16071107

Cited by 31 publications

(13 citation statements)

References 20 publications

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“…Through merging pixels, the number of superpixels in the image is largely decreased, which greatly reduces the burden of subsequent processing and, therefore, improves the efficiency. Owing to these advantages, the superpixel has become an important part of computer vision algorithms and has been widely studied [13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Wang

Shen

2020

Brain Sciences

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Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

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Section: Related Workmentioning

confidence: 99%

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Wang

Shen

2020

Brain Sciences

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“…Here we choose the SLIC method for initial segmentation since it can produce superpixels with similar size and obtain better edge preservation. For detail information about SLIC, please refer to [49], [51], [52]. To suppress speckle noises and reduce computation time, a superpixel-based joint sparse feature representation is extracted as the initial features of the topic model.…”

Section: ) Superpixel-based Multi-feature Joint Sparse Representationmentioning

confidence: 99%

A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification

Shi

Jin

2020

IEEE Access

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Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important study areas for PolSAR image processing. Many kinds of PolSAR features can be extracted for PolSAR image classification, such as the scattering, polarimetric or image features. However, it is difficult to improve the classification accuracy of PolSAR images by using all these low-level features directly, since they may conflict with each other for classification. Hence, how to joint learn these lowlevel features to obtain high-level discriminating features is a challenging task. To solve this problem, a novel fast multi-feature joint learning method(fMF-JLC) is proposed for PolSAR image classification. The proposed method extract three kinds of low-level features of PolSAR data at first. Then, a multifeature joint sparse representation model(MF-JSR) is proposed by designing joint sparse constraints on the extracted features above. Moreover, the joint sparse features are further compressed to overcome the dimension curse and acquire semantic features by the topic model. By this way, the low-level features are fused and discriminating high-level features are acquired. However, the pixel-wise feature learning method is time consuming. To speed the proposed method, a superpixel-based fast learning method is designed by involving the contextual relationship. Experiments are taken on three sets of real PolSAR data with different sensors and bands, and several compared methods are used to verify the effectiveness of the proposed method. The experimental results illustrate that the proposed method can obtain better performance than the state-ofart methods, especially for the heterogenous areas. INDEX TERMS Polarimetric SAR classification, joint multi-feature sparse representation, joint multifeature learning, fast classification method.

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“…Since the SLIC [25] method was proposed, scholars who study SAR images devised a number of superpixel generating algorithms, such as PILS [26], LBSLIC [27], and PBSLIC [28], based on the core idea of the SLIC method.…”

Section: Related Workmentioning

confidence: 99%

“…They defined the pixel intensity similarity, which is robust for speckle noise; then, the pixel intensity and location similarities were used to generate superpixels. In 2016, Zou et al [27] proposed a likelihood-based SLIC (LBSLIC) superpixel algorithm for SAR images. They believed that the likelihood information instead of the mean intensity of SAR image clusters is more useful due to the inherent speckle noise; thus, the likelihood value rather than the Euclidean distance was adopted to represent the intensity similarity between a pixel and a cluster.…”

Section: Introductionmentioning

confidence: 99%

PSDSD-A Superpixel Generating Method Based on Pixel Saliency Difference and Spatial Distance for SAR Images

Xie

Huang

Shi

et al. 2019

Sensors

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Superpixel methods are widely used in the processing of synthetic aperture radar (SAR) images. In recent years, a number of superpixel algorithms for SAR images have been proposed, and have achieved acceptable results despite the inherent speckle noise of SAR images. However, it is still difficult for existing algorithms to obtain satisfactory results in the inhomogeneous edge and texture areas. To overcome those problems, we propose a superpixel generating method based on pixel saliency difference and spatial distance for SAR images in this article. Firstly, a saliency map is calculated based on the Gaussian kernel function weighted local contrast measure, which can not only effectively suppress the speckle noise, but also enhance the fuzzy edges and regions with intensity inhomogeneity. Secondly, superpixels are generated by the local k-means clustering method based on the proposed distance measure, which can efficiently sort pixels to different clusters. In this step, the distance measure is calculated by combining the saliency difference and spatial distance with a proposed adaptive local compactness parameter. Thirdly, post-processing is utilized to clean up small segments. The evaluation experiments on the simulated SAR image demonstrate that our proposed method dramatically outperforms four state-of-the-art methods in terms of boundary recall, under-segmentation error, and achievable segmentation accuracy under almost all of the experimental parameters at a moderate segment speed. The experiments on real-world SAR images of different sceneries validate the superiority of our method. The superpixel results of the proposed method adhere well to the contour of targets, and correctly reflect the boundaries of texture details for the inhomogeneous regions.

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A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution

Cited by 31 publications

References 20 publications

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification

PSDSD-A Superpixel Generating Method Based on Pixel Saliency Difference and Spatial Distance for SAR Images

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