A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance

Zhang, Yue; Zou, Huanxin; Luo, Tiancheng; Qin, Xianxiang; Zhou, Shilin; Ji, Kefeng

doi:10.3390/s16101687

Cited by 25 publications

(15 citation statements)

References 28 publications

(38 reference statements)

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“…In contrast, much better segmentation results are obtained in Figure 4 d. Accurate class boundaries are achieved in areas such as East Lake (area C of Figure 4 d), the urban areas in the Fuhushan Community Neighborhood (area D of Figure 4 d), the forest in Nanwang Mountain (area E of Figure 4 d), and the bridge (area F of Figure 4 d). This is because the revised Wishart distance can accurately characterize the similarity between covariance matrices [ 39 , 45 ], which contributes to the precise determination of the homogeneous regions.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In the proposed method, Bhattacharya decomposition [ 38 ] is chosen to decide the dominant scattering mechanism of the pixels. For two adjacent pixels of the same scattering mechanism, we compute the revised Wishart distance [ 39 ]. Finally, we merge the two adjacent pixels if the revised Wishart distance is smaller than the threshold.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance

Shi

et al. 2018

Sensors

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A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used to decide the merging order. The merging predicate is determined by the scattering characteristics and the revised Wishart distance between adjacent pixels, which can greatly improve the performance in speckle suppression and detail preservation. A postprocessing step is applied to obtain a satisfactory result after the merging operation. The decomposition and merging processes are iteratively executed until the termination criterion is met. The superiority of the proposed method was verified with experiments on two RADARSAT-2 PolSAR images and a Gaofen-3 PolSAR image, which demonstrated that the proposed method can obtain more accurate segmentation results and shows a better performance in speckle suppression and detail preservation than the other algorithms.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance

Shi

et al. 2018

Sensors

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“…Given the absence of statistical characteristics for PolSAR data, the statistical feature is introduced into the FNEA framework in the proposed approach. Many other methods use the classical complex Wishart distribution in order to represent scattering matrix statistics for PolSAR image segmentation [15,17,[19][20][21]27]. Considering the ability to modeling varying degrees of texture [30,36], G 0 distribution is more suitable for heterogeneous or homogeneous areas in high-resolution PolSAR data compared to the Wishart or K distribution.…”

Section: Main Features Of the Proposed Methodsmentioning

confidence: 99%

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Chen

et al. 2017

Remote Sensing

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Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of high-resolution PolSAR image segmentation. A novel segmentation algorithm is proposed in this study in order to address the problem and to obtain accurate and precise segmentation results. This method integrates statistical features into a fractal net evolution algorithm (FNEA) framework, and incorporates polarimetric features into a simple linear iterative clustering (SLIC) superpixel generation algorithm. First, spectral heterogeneity in the traditional FNEA is substituted by the G 0 distribution statistical heterogeneity in order to combine the shape and statistical features of PolSAR data. The statistical heterogeneity between two adjacent image objects is measured using a log likelihood function. Second, a modified SLIC algorithm is utilized to generate compact superpixels as the initial samples for the G 0 statistical model, which substitutes the polarimetric distance of the Pauli RGB composition for the CIELAB color distance. The segmentation results were obtained by weighting the G 0 statistical feature and the shape features, based on the FNEA framework. The validity and applicability of the proposed method was verified with extensive experiments on simulated data and three real-world high-resolution PolSAR images from airborne multi-look ESAR, spaceborne single-look RADARSAT-2, and multi-look TerraSAR-X data sets. The experimental results indicate that the proposed method obtains more accurate and precise segmentation results than the other methods for high-resolution PolSAR images.

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“…Therefore, these methods can overcome the effect of speckle noise in PolSAR images as well as with high computation efficiency. In this paper, a fast superpixel segmentation method called Pol-IER [33], which was proposed in our previous study, is utilized to generate superpixels with good boundary adherence and regular shape in the homogeneous regions of PolSAR images for the subsequent processing.…”

Section: Introductionmentioning

confidence: 99%

“…To address the discriminability loss problem caused by the concatenation of multiple feature vectors, we propose a superpixel-based unsupervised classification framework for PolSAR images based on CSNF. To take advantage of region information and overcome problems caused by speckle noise, the PolSAR image is first oversegmented into many superpixels by Pol-IER [33]. Second, five feature vectors, including the Krogager decomposition feature vector [35], the Yamaguchi4 decomposition feature vector [36], the Cloude-Pottier's decomposition feature vector [37], the HSI color feature vector and a feature vector stacked by scattering power entropy and the copolarized ratio [38], are extracted based on superpixels, and five corresponding similarity matrices are constructed by using a Gaussian kernel.…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Oriented Unsupervised Classification for Polarimetric SAR Images Based on Consensus Similarity Network Fusion

Zou

Shao

et al. 2019

IEEE Access

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Unsupervised polarimetric synthetic aperture radar (PolSAR) image classification is an important task in PolSAR automatic image analysis and interpretation. Generally, a group of features is insufficient to effectively classify PolSAR images, especially in multiple terrain scenarios. Therefore, multiple features need to be extracted for PolSAR image classification. However, how to combine and integrate these features effectively to fully utilize each feature's information and discriminability need to be determined. Such integrated work has traditionally received little attention. In this paper, a novel unsupervised classification framework for PolSAR images is proposed. First, a PolSAR image is oversegmented via a fast superpixel segmentation method. Second, five feature vectors are extracted from PolSAR images via superpixels, resulting in five corresponding similarity matrices that are constructed by using Gaussian kernels. Third, consensus similarity network fusion (CSNF), originally proposed and widely used for biomedical sciences, is employed to combine and integrate the five similarity matrices to obtain a fused similarity matrix. Fourth, spectral clustering method, based on the fused similarity matrix, is used to cluster the PolSAR image. Finally, a novel classification postprocessing procedure is presented and exploited to smooth the initial clusters and correct some misclassified pixels. The extensive experimental results conducted on one simulated and two real-world PolSAR images demonstrate the feasibility and superiority of the proposed method compared with five other state-of-the-art classification approaches. INDEX TERMS Polarimetric synthetic aperture radar (PolSAR) images, unsupervised classification, superpixels segmentation, consensus similarity network fusion (CSNF), spectral clustering.

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A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance

Cited by 25 publications

References 28 publications

A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance

A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Superpixel-Oriented Unsupervised Classification for Polarimetric SAR Images Based on Consensus Similarity Network Fusion

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