Infrared Technology, Volume. 47, Issue 3, 335(2025)

Hyperspectral Image Clustering Algorithm Based on Spectral Unmixing and Dynamic Weighted Diffusion Mapping

Yuancheng HUANG and Xinyu GAO
Author Affiliations
  • College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
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    References(22)

    [2] [2] Appice A, Guccione P, Acciaro E, et al. Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification[J]. Applied Intelligence, 2020, 50(10): 1-22.

    [3] [3] Makantasis K, Doulamis A D, Doulamis N D, et al. Tensor-based classification models for hyperspectral data analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(12): 6884-6898.

    [4] [4] SUN L, WU F, HE C, et al. Weighted collaborative sparse and L1/2 low-rank regularizations with superpixel segmentation for hyperspectral unmixing[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 19: 1-5.

    [5] [5] Francois V, Olivier B, Stefania M. Anomaly detection for replacement model in hyperspectral imaging[J]. Signal Processing, 2021, 185: 108079.

    [8] [8] ZHANG Hongyan, ZHAI Han, ZHANG Liangpei, et al. Spectral-spatial sparse subspace clustering for hyperspectral remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6): 3672-3684.

    [9] [9] ZHAI H, ZHANG H, LI P, et al. Hyperspectral image clustering: Current achievements and future lines[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(4): 35-67.

    [10] [10] Nadler B, Lafon S, Coifman R R, et al. Diffusion maps, spectral clustering and reaction coordinates of dynamical systems[J]. Applied and Computational Harmonic Analysis, 2005, 21(1): 113-127.

    [11] [11] Singer A, WU H T. Vector diffusion maps and the connection Laplacian[J]. Communications on Pure and Applied Mathematics, 2012, 65(8): 1067-1144.

    [12] [12] CoifmanR R, KevrekidisG I, LafonS, et al. Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems[J]. Multiscale Modeling Simulation, 2008, 7(2): 842-864.

    [13] [13] De la Porte J, Herbst B, Hereman W, et al. An introduction to diffusion maps[C]//Proceedings of the 19th Symposium of the Pattern Recognition Association of South Africa (PRASA 2008), 2008: 15-25.

    [14] [14] Murphy M J, Maggioni M. Unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(3): 1829-1845.

    [15] [15] Polk S L, Murphy J M. Multiscale clustering of hyperspectral images through spectral-spatial diffusion geometry[C]//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021: 4688-4691.

    [16] [16] CUI K, LI R, Polk S L, et al. Unsupervised spatial-spectral hyperspectral image reconstruction and clustering with diffusion geometry[C]//2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2022: 1-5, Doi: 10.1109/WHISPERS56178.2022.9955069.

    [17] [17] CHEN J, LIU S, ZHANG Z, et al. Diffusion subspace clustering for hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 6517-30.

    [19] [19] Bioucas Dias J M, Nascimento J M P. Hyperspectral subspace identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8): 2435-2445.

    [20] [20] Boardman J W, Kruse F A, Green R O. Mapping target signature via partial unmixing of AVIRIS data[C]//Fifth JPL Airborne Earth Science Workshop, 1995: 23-26.

    [21] [21] Heinz D C, CHANG C I. Fully Constrained least square liner spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Transaction on Geoscience and Remote Sensing, 2001 (39-3): 529-545.

    [22] [22] Hastie T, Tibshirani R, Friedman J H, et al. The elements of statistical learning: data mining, inference, and prediction[M]. New York: Springer, 2009.

    [23] [23] Voorhees E M. Implementing agglomerative hierarchic clustering algorithms for use in document retrieval[J]. Information Processing & Management, 1986, 22(6): 465-476.

    [24] [24] Selim S Z, Ismail M A. K-means-type algorithms: a generalized convergence theorem and characterization of local optimality[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984(1): 81-87.

    [25] [25] Von Luxburg U. A tutorial on spectral clustering[J]. Statistics and Computing, 2007, 17: 395-416.

    [26] [26] Abdolali M, Gillis N. Beyond linear subspace clustering: a comparative study of nonlinear manifold clustering algorithms[J]. Computer Science Review, 2021, 42: 100435.

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    HUANG Yuancheng, GAO Xinyu. Hyperspectral Image Clustering Algorithm Based on Spectral Unmixing and Dynamic Weighted Diffusion Mapping[J]. Infrared Technology, 2025, 47(3): 335

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    Paper Information

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    Received: May. 28, 2024

    Accepted: Apr. 18, 2025

    Published Online: Apr. 18, 2025

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