Citation: | HUANG Yuancheng, GAO Xinyu. Hyperspectral Image Clustering Algorithm Based on Spectral Unmixing and Dynamic Weighted Diffusion Mapping[J]. Infrared Technology , 2025, 47(3): 335-341. |
The traditional hyperspectral image (HSI) clustering algorithm suffers from issues, such as poor accuracy. In addition, accurately measuring the similarity relationship between pixels using long-time and commonly used distance-measurement criteria is difficult. To improve the clustering performance of hyperspectral images, this study proposes a hyperspectral image clustering algorithm based on spectral unmixing and dynamic-weighted diffusion mapping. The algorithm is based on the decomposition of mixed pixels and diffusion distance, calculated using the diffusion mapping theory. The proposed method uses the high-dimensional geometry and abundance structure observed in hyperspectral images to solve the clustering problem. Experimental results on two real hyperspectral datasets showed that the proposed algorithm has high classification accuracy and can be successfully applied to hyperspectral image clustering.
[1] |
张兵. 高光谱图像处理与信息提取前沿[J]. 遥感学报, 2016, 20(5): 1062-1090.
ZHANG B. Advancement of hyperspectral image processing and information extraction[J]. Journal of Remote Sensing, 2016, 20(5): 1062-1090.
|
[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. http://www.xueshufan.com/publication/3022274771
|
[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. DOI: 10.1109/TGRS.2018.2845450
|
[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] |
Francois V, Olivier B, Stefania M. Anomaly detection for replacement model in hyperspectral imaging[J]. Signal Processing, 2021, 185: 108079. DOI: 10.1016/j.sigpro.2021.108079
|
[6] |
姚云军, 秦其明, 张自力, 等. 高光谱技术在农业遥感中的应用研究进展[J]. 农业工程学报, 2008, 24(7): 301-306. DOI: 10.3321/j.issn:1002-6819.2008.07.063
YAO Y J, QIN Q M, ZHANG Z L, et al. Research progress of hyperspectral technology applied in agricultural remote sensing[J]. Transactions of the CSAE, 2008, 24(7): 301-306. DOI: 10.3321/j.issn:1002-6819.2008.07.063
|
[7] |
童庆禧, 张兵, 张立福. 中国高光谱遥感的前沿进展[J]. 遥感学报, 2016, 20(5): 689-707.
TONG Q X, ZHANG B, ZHANG L F. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 2016, 20(5): 689-707.
|
[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. DOI: 10.1109/TGRS.2016.2524557
|
[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. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9328197
|
[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] |
Singer A, WU H T. Vector diffusion maps and the connection Laplacian[J]. Communications on Pure and Applied Mathematics, 2012, 65(8): 1067-1144. DOI: 10.1002/cpa.21395
|
[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. DOI: 10.1137/070696325
|
[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] |
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. DOI: 10.1109/TGRS.2018.2869723
|
[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] |
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] |
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. DOI: 10.1109/JSTARS.2023.3294623
|
[18] |
张兵. 高光谱图像混合像元分解[J]. 遥感学报, 2023, 27(12): 2882-2883.
ZHANG B. Hyperspectral image spectral unmixing[J]. Journal of Remote Sensing, 2023, 27(12): 2882-2883.
|
[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. DOI: 10.1109/TGRS.2008.918089
|
[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] |
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] |
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] |
Voorhees E M. Implementing agglomerative hierarchic clustering algorithms for use in document retrieval[J]. Information Processing & Management, 1986, 22(6): 465-476. http://dspace.library.cornell.edu/bitstream/1813/6605/1/86-765.pdf
|
[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] |
Von Luxburg U. A tutorial on spectral clustering[J]. Statistics and Computing, 2007, 17: 395-416. DOI: 10.1007/s11222-007-9033-z
|
[26] |
Abdolali M, Gillis N. Beyond linear subspace clustering: a comparative study of nonlinear manifold clustering algorithms[J]. Computer Science Review, 2021, 42: 100435. DOI: 10.1016/j.cosrev.2021.100435
|
[1] | LI Minglu, WANG Xiaoxia, HOU Maoxin, YANG Fengbao. An Object Detection Algorithm Based on Infrared-Visible Feature Enhancement and Fusion[J]. Infrared Technology , 2025, 47(3): 385-394. |
[2] | JIN Dan, LIU Xiaoguang, SHI Gang, SONG Renping, ZU Mingxia. 3D Point Cloud Registration Method for Substation Robot Patrol Tracks[J]. Infrared Technology , 2023, 45(6): 678-684. |
[3] | DING Jian, GAO Qingwei, LU Yixiang, SUN Dong. Infrared and Visible Image Fusion Algorithm Based on the Decomposition of Robust Principal Component Analysis and Latent Low Rank Representation[J]. Infrared Technology , 2022, 44(1): 1-8. |
[4] | HAN Tuanjun, YIN Jiwu. Robust Adaptive Updating Strategy for Missile-borne Infrared Object-tracking Algorithm[J]. Infrared Technology , 2018, 40(7): 625-631. |
[5] | YAO Zhaoxia, XIE Tao. Robust Small Dim Object CFA Detection Algorithm Based on Local Contrast Measure in Aerial Complex Background[J]. Infrared Technology , 2017, 39(10): 940-945. |
[6] | YANG Zhixiong, YU Chunchao, YAN Min, YUAN Xiaochun, ZENG Bangze, SU Yulu. Particle Filter Infrared Target Tracking Algorithm Based on Feature Fusion[J]. Infrared Technology , 2016, 38(3): 211-217. |
[7] | LIU Huan, GU Xiaojing, GU Xingsheng. Thermal Image Stitching Based on Robust Feature Matching[J]. Infrared Technology , 2016, 38(1): 10-20. |
[8] | ZHANG Shuang-lei, CHEN Fan-sheng, WANG Tao. A Dim Small Target Detection Algorithm Based on Multi-Features Fusion Algorithm[J]. Infrared Technology , 2015, (8): 635-641. |
[9] | ZHANG Sheng, LI Yu-feng, YAN Yun-yang, XU Ming-wei, XIONG Ping, TANG Zun-lie. Moving Target Detection Using Fusion of Visual and Thermal Video for Robust Surveillance[J]. Infrared Technology , 2013, (12): 773-779. |
[10] | XING Su-xia, ZHANG Jun-jv, CHANG Ben-kang. Image Fusion Method Based on NSCT and Robustness Analysis[J]. Infrared Technology , 2011, 33(1): 45-48,55. DOI: 10.3969/j.issn.1001-8891.2011.01.011 |