Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (02): 39-44.doi: 10.12052/gdutxb.210191

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A Spectral Clustering Algorithm Based on Fréchet Distance

Fan Juan1, Deng Xiu-qin1, Liu Yu-lan1,2   

  1. 1. School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China;
    2. State Key Lab for NoveI Software Technology, Nanjing University, Nanjing 210093, China
  • Received:2021-12-02 Online:2023-03-25 Published:2023-04-07

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Abstract: In order to improve the clustering accuracy and applicability of spectral clustering, a spectral clustering algorithm based on Fréchet distance (called FSC) is proposed. Firstly a similarity matrix is constructed by Fréchet distance, then the reconstructed similarity matrix is applied to the spectral clustering. Using Fréchet distance to measure the similarity of data feature dimensions can extend the applicability of typical spectral clustering algorithms. FSC is not only suitable for data with clear low-dimensional manifold structure, but also for high-dimensional or sparse data, such as hyperspectral images. The experimental results on three hyperspectral images show that the FSC algorithm effectively improves the accuracy of hyperspectral images clustering.

Key words: hyperspectral images, Fréchet distance, clustering, affinity matrix

CLC Number: 

  • TP751.1
[1] ZHU M, HUANG D, HU X J, et al. Application of hyperspectral technology in detection of agricultural products and food: a review [J]. Food Science & Nutrition, 2020, 8(10): 5206-5214.
[2] 马少鹏, 梁路, 滕少华. 一种轻量级的高光谱遥感图像分类方法[J]. 广东工业大学学报, 2021, 38(3): 29-35.
MA S P, LIANG L, TENG S H. A lightweight hyperspectral remote sensing image classification method [J]. Journal of Guangdong University of Technology, 2021, 38(3): 29-35.
[3] ALSAMHI S H, MA O. Collaboration of drone and internet of public safety things in smart cities: an overview of QoS and network performance optimization [J]. Drones, 2019, 3(1): 13.
[4] KUMAR V S, SIVAPRAKASAM S A, NAGANATHAN R, et al. Fast K-means technique for hyper-spectral image segmentation by multiband reduction [J]. Pollack Periodica, 2019, 14(3): 201-212.
[5] SALEM R B, ETTABAA K S, HAMDI M A. Fuzzy C-mean for unsupervised spectral-spatial SVM classification of hyperspectral images[C]//2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). Piscataway, NJ: IEEE, 2017: 759-765.
[6] SHAHID K T, MALHOTRA A, SCHIZAS I D, et al. Unsupervised kernel correlations based hyperspectral clustering with missing pixels [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(6): 1799-1810.
[7] WANG R, NIE F, YU W. Fast spectral clustering with anchor graph for large hyperspectral images [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(11): 2003-2007.
[8] YE J, ZHAO Z, LIU H. Adaptive distance metric learning for clustering[C]//2007 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2007: 1-7.
[9] SUN W, PENG J, YANG G, et al. Correntropy-based sparse spectral clustering for hyperspectral band selection [J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(3): 484-488.
[10] ZHAO Y, YUAN Y, WANG Q. Fast spectral clustering for unsupervised hyperspectral image classification [J]. Remote Sensing, 2019, 11(4): 399.
[11] ZHAI H, ZHANG H, ZHANG L, et al. A new sparse subspace clustering algorithm for hyperspectral remote sensing imagery [J]. IEEE Geoscience and Remote Sensing Letters, 2016, 14(1): 43-47.
[12] 龙咏红, 邓秀勤, 王卓薇. 基于信息熵的加权块稀疏子空间聚类算法[J]. 数据采集与处理, 2021, 36(3): 544-555.
LONG Y H, DENG X Q, WANG Z W. Weighted block sparse subspace clustering algorithm based on information entropy [J]. Journal of Data Acquisition and Processing, 2021, 36(3): 544-555.
[13] YAN Q, DING Y, ZHANG J J, et al. A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery [J]. Cognitive Systems Research, 2019, 53: 98-110.
[14] WENG H, WANG S, WAN Y, et al. Discrete Fréchet distance algorithm based criterion of transformer differential protection with the immunity to saturation of current transformer [J]. International Journal of Electrical Power & Energy Systems, 2020, 115: 105449.
[15] BANG Y, KIM J, YU K. An improved map-matching technique based on the Fréchet distance approach for pedestrian navigation services [J]. Sensors, 2016, 16(10): 1768.
[16] FRÉCHET M M. Sur quelques points du calcul fonctionnel [J]. Rendiconti del Circolo Matematico di Palermo (1884-1940), 1906, 22(1): 1-72.
[17] 张文昊. 弗雷歇距离判断曲线相似度的嵌入式模块[J]. 单片机与嵌入式系统应用, 2020, 20(9): 17-20.
ZHANG W H. Embedded module of curve similarity judging based on Fréchet distance [J]. Microcontrollers Embedded Systems, 2020, 20(9): 17-20.
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