Efficient Superpixel Generation for Polarimetric SAR Images with Cross-Iteration and Hexagonal Initialization

Li, Meilin; Zou, Huanxin; Qin, Xianxiang; Dong, Zhen; Sun, Li; Wei, Juan

doi:10.3390/rs14122914

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…To further study the performance of MOES, we compare it with six popular superpixel segmentation approaches, which are SLIC [9], SEEDS [10], TP [11], QS [14], POL-HLT [50], and HCI [51]. Among them, POL-HLT is a PolSAR superpixel segmentation method with improved SLIC, where an improved initialization and a modified distance metric to Hotelling-Lawley trace distance are introduced.…”

Section: Comparison Experiments On Polsar Datasetsmentioning

confidence: 99%

Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification

Chu,

Zhang,

et al. 2024

Remote Sensing

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Superpixel segmentation has been widely used in the field of computer vision. The generations of PolSAR superpixels have also been widely studied for their feasibility and high efficiency. The initial numbers of PolSAR superpixels are usually designed manually by experience, which has a significant impact on the final performance of superpixel segmentation and the subsequent interpretation tasks. Additionally, the effective information of PolSAR superpixels is not fully analyzed and utilized in the generation process. Regarding these issues, a multiobjective evolutionary superpixel segmentation for PolSAR image classification is proposed in this study. It contains two layers, an automatic optimization layer and a fine segmentation layer. Fully considering the similarity information within the superpixels and the difference information among the superpixels simultaneously, the automatic optimization layer can determine the suitable number of superpixels automatically by the multiobjective optimization for PolSAR superpixel segmentation. Considering the difficulty of the search for accurate boundaries of complex ground objects in PolSAR images, the fine segmentation layer can further improve the qualities of superpixels by fully using the boundary information of good-quality superpixels in the evolution process for generating PolSAR superpixels. The experiments on different PolSAR image datasets validate that the proposed approach can automatically generate high-quality superpixels without any prior information.

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Section: Comparison Experiments On Polsar Datasetsmentioning

confidence: 99%

Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification

Chu,

Zhang,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, the common Euclidean space norm distance ignores the manifold structure. Therefore, in many literatures, geodesic distance is a better choice for measuring PolSAR data [24], [30]. The geodesic distance [31] based on the Kennaugh matrix can be defined as:…”

Section: B Geodesic Distancementioning

confidence: 99%

“…To further reduce the computation cost, a fast implementation of the Tr Y −1 X is adopted [30]. Let w = f ((X −1 ) T ) and t = f (X), where f (•) is a function that arranges all the elements of the matrix into a vector.…”

Section: Generalized Likelihood Ratio Test Distancementioning

confidence: 99%

“…In this article, the previously proposed superpixel segmentation method is directly adopted [30] to obtain superpixels. The cross-iteration strategy can not only reduce the operation burden but also fully exploit the advantages of different distance measures.…”

Section: A Superpixel Segmentationmentioning

confidence: 99%

“…In addition, the previously proposed method can generate superpixels with higher edge adherence and has higher computational efficiency. The detailed superpixel segmentation method for PolSAR images is referred to in the literature [30], and will not be repeated here.…”

Section: A Superpixel Segmentationmentioning

confidence: 99%

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Unsupervised Semantic Segmentation of PolSAR Images Based on Multiview Similarity

Li,

Zou,

Dong

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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Semantic segmentation is an essential task in polarimetric synthetic aperture radar (PolSAR) image interpretation. To address the issue of insufficient measurement ability of singleview similarity, an unsupervised semantic segmentation method for PolSAR images based on multiview similarity is proposed to estimate the number-of-classes (NoC) and perform classification. NoC estimation is commonly neglected in semantic segmentation methods, to ensure rationality, a NoC estimation method before clustering is proposed based on multiview polarimetric rotation domain features and the visual assessment of tendency (VAT) method for PolSAR images without supervision. Then, the norm distance, geodesic distance, maximum likelihood distance and generalized likelihood ratio test distance based on the statistical characteristics of PolSAR images are comprehensively analyzed. Various advantages of different distances are integrated to combine the multiview vector information and scattering information to construct six multi-kernel similarity matrices. Subsequently, the consensus similarity network fusion method is utilized to further strengthen the discriminative ability of the similarity matrices. In addition, efficient superpixel segmentation is also adopted to reduce the speckle noise. Finally, based on the estimated NoC and the fused similarity matrix, spectral clustering is utilized to obtain semantic segmentation results. Extensive experiments conducted on two AIRSAR data sets and one Gaofen-3 data set demonstrate that the proposed method can effectively combine the spatial neighborhood similarity information and achieve higher semantic segmentation accuracy.

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Distance Measures of Polarimetric SAR Image Data: A Survey

et al. 2022

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Distance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarized. In order to make better use of these distance measures in algorithm design, this paper provides a systematic survey of them and analyzes their relations in detail. We divide these distance measures into five main categories (i.e., the norm distances, geodesic distances, maximum likelihood (ML) distances, generalized likelihood ratio test (GLRT) distances, stochastics distances) and two other categories (i.e., the inter-patch distances and those based on metric learning). Furthermore, we analyze the relations between different distance measures and visualize them with graphs to make them clearer. Moreover, some properties of the main distance measures are discussed, and some advice for choosing distances in algorithm design is also provided. This survey can serve as a reference for researchers in PolSAR image processing, analysis, and related fields.

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Efficient Superpixel Generation for Polarimetric SAR Images with Cross-Iteration and Hexagonal Initialization

Cited by 4 publications

References 35 publications

Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification

Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification

Unsupervised Semantic Segmentation of PolSAR Images Based on Multiview Similarity

Distance Measures of Polarimetric SAR Image Data: A Survey

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