高光谱影像逆近邻密度峰值聚类的波段选择算法

doi:10.11947/j.AGCS.2024.20220350

测绘学报 ›› 2024, Vol. 53 ›› Issue (1): 8-19.doi: 10.11947/j.AGCS.2024.20220350

高光谱影像逆近邻密度峰值聚类的波段选择算法

孙根云^1,2, 李忍忍¹, 张爱竹^1,2, 安娜³, 付航¹, 潘兆杰¹

1. 中国石油大学(华东)海洋与空间信息学院, 山东青岛 266580;
2. 海洋国家实验室海洋矿产资源评价与探测技术功能实验室, 山东青岛 266071;
3. 中国自然资源航空物探遥感中心, 北京 100083

收稿日期:2022-05-26 修回日期:2023-06-04 发布日期:2024-02-06
通讯作者: 安娜 E-mail:an_na826@163.com
作者简介:孙根云(1979-),男,博士,教授,博士生导师,主要研究方向智能遥感、环境遥感。E-mail:genyunsun@163.com
基金资助:
国家自然科学基金(42271347;41971292);科技部国家重点研发计划(2019YFE26700)

Band selection algorithm for reverse nearest neighbor density peak clustering of hyperspectral images

SUN Genyun^1,2, LI Renren¹, ZHANG Aizhu^1,2, AN Na³, FU Hang¹, PAN Zhaojie¹

1. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China;
2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China;
3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China

Received:2022-05-26 Revised:2023-06-04 Published:2024-02-06
Supported by:
The National Natural Science Foundation of China (Nos. 42271347;41971292); The National Key Research and Development Program of China (No. 2019YFE26700)

摘要/Abstract

摘要： 密度峰值聚类波段选择算法利用局部密度描述波段的密度信息,然而现有的局部密度容易忽略波段分布的全局信息,不能有效描述波段的分布特征,导致波段子集分类精度有限。为解决上述问题,本文提出一种基于逆近邻的密度峰值聚类波段选择算法。首先,利用波段与其K近邻构建K近邻有向图,获取波段的逆近邻,以及波段之间的共享近邻和共享逆近邻;然后,利用共享近邻和共享逆近邻并集的个数作为波段之间的相似度,利用波段与其逆近邻的平均欧氏距离和相似度构造增强型局部密度;最后,将增强型局部密度、距离因子、信息熵三者的乘积作为权重值,根据权重值挑选波段子集。为提高试验效率和实用性,本文算法还提出一种自动获得K值的自适应K值方法。在3个高光谱标准数据集上的试验结果表明,本文算法得到的波段子集比其他先进算法挑选的波段有更好的分类性能,尤其是在波段数较少的情况下,而且计算效率较高。

关键词: 高光谱影像, 波段选择, 密度峰值聚类, 逆近邻, 局部密度, 自适应K值

Abstract: The density peak clustering band selection algorithm uses the local density to describe the density information of the band. However, the existing local density is easy to ignore the global information of the band distribution and can't effectively describe the distribution characteristics of the band, resulting in the limited classification accuracy of the band subset. In order to solve the above problems, this paper proposes a density peak clustering band selection algorithm based on reverse nearest neighbor. Firstly, the K-nearest neighbor directed graph is constructed by using the band and its K-nearest neighbor to obtain the reverse nearest neighbor of the band, as well as the shared nearest neighbor and shared reverse nearest neighbor between bands. Then, the union number of shared nearest neighbors and shared reverse nearest neighbors is used as the similarity between bands, and the enhanced local density is constructed by using the average Euclidean distance and similarity between bands and their reverse nearest neighbors. Finally, the product of enhanced local density, distance factor and information entropy is taken as the weight value, and the segment subset is selected according to the weight value. In order to improve the efficiency and practicability of the experiment, an adaptive K value method is also proposed in this paper. The experimental results on three hyperspectral standard data sets show that the band subset obtained by this algorithm has better classification performance than the band selected by other advanced algorithms, especially when the number of bands is small, and the calculation efficiency is high.

Key words: hyperspectral image, band selection, density peak clustering, reverse nearest neighbor, local density, adaptive K value

中图分类号:

P237

孙根云, 李忍忍, 张爱竹, 安娜, 付航, 潘兆杰. 高光谱影像逆近邻密度峰值聚类的波段选择算法[J]. 测绘学报, 2024, 53(1): 8-19.

SUN Genyun, LI Renren, ZHANG Aizhu, AN Na, FU Hang, PAN Zhaojie. Band selection algorithm for reverse nearest neighbor density peak clustering of hyperspectral images[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(1): 8-19.

参考文献

[1] 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 Qingxi, ZHANG Bing, ZHANG Lifu. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sen-sing, 2016, 20(5): 689-707.
[2] WANG Kexian, ZHENG Shunyi, LI Rui, et al.A deep double-channel dense network for hyperspectral image classifica-tion[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4): 46-62.
[3] HUGHES G. On the mean accuracy of statistical pattern recognizers[J]. IEEE Transactions on Information Theory, 1968, 14(1): 55-63.
[4] 谷雨, 徐英, 郭宝峰. 融合空谱特征和集成超限学习机的高光谱图像分类[J]. 测绘学报, 2018, 47(9): 1238-1249.DOI: 10.11947/j.AGCS.2018.20170476. GU Yu, XU Ying, GUO Baofeng. Hyperspectral image classification by combination of spatial-spectral features and ensemble extreme learning machines[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(9): 1238-1249.DOI: 10.11947/j.AGCS.2018.20170476.
[5] 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 Bing. Advancement of hyperspectral image processing and information extraction[J]. Journal of Remote Sensing, 2016, 20(5):1062-1090.
[6] ZHENG Xiangtao, YUAN Yuan, LU Xiaoqiang. Dimensionality reduction by spatial-spectral preservation in selected bands[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(9): 5185-5197.
[7] HANG Renlong, LIU Qingshan. Dimensionality reduction of hyperspectral image using spatial regularized local graph discriminant embedding[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(9): 3262-3271.
[8] SUN Weiwei, DU Qian. Hyperspectral band selection: a review[J]. IEEE Geoscience and Remote Sensing Magazine, 2019, 7(2): 118-139.
[9] 张红, 吴智伟, 王继成, 等. 高光谱图像分类的Wasserstein配置熵非监督波段选择方法[J]. 测绘学报, 2021, 50(3): 405-415.DOI: 10.11947/j.AGCS.2021.20200006. ZHANG Hong, WU Zhiwei, WANG Jicheng, et al. Unsupervised band selection for hyperspectral image classification using the Wasserstein metric-based configuration entropy[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3): 405-415.DOI: 10.11947/j.AGCS.2021.20200006.
[10] CHANG C I, DU Q, SUN T L, et al. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(6): 2631-2641.
[11] CHANG C I, WANG S. Constrained band selection for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6): 1575-1585.
[12] XU Buyun, LI Xihai, HOU Weijun, et al. A similarity-based ranking method for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9585-9599.
[13] AHMAD M, HAQ I, MUSHTAQ Q, et al. A new statistical approach for band clustering and band selection using K-means clustering[J]. International Journal of Engineering Technology, 2011, 3(6): 606-614.
[14] MARTÍNEZ-USÓMARTINEZ-USO A, PLA F, SOTOCA J M, et al. Clustering-based hyperspectral band selection using information measures[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(12): 4158-4171.
[15] WANG Qi, ZHANG Fahong, LI Xuelong.Optimal clustering framework for hyperspectral band selection[EB/OL].[2023-02-10]. https://arxiv.org/abs/1904.13036.pdf.
[16] WANG Jun, TANG Chang, ZHENG Xiao, et al.Graph regularized spatial-spectral subspace clustering for hyperspectral band selection[J]. Neural Networks, 2022, 153: 292-302.
[17] ZHANG Aizhu, MA Ping, LIU Sihan, et al. Hyperspectral band selection using crossover-based gravitational search algorithm[J]. IET Image Processing, 2019, 13(2): 280-286.
[18] GENG Xiurui, SUN Kang, JI Luyan, et al. A fast volume-gradient-based band selection method for hyperspectral image[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11): 7111-7119.
[19] WANG Qi, ZHANG Fahong, LI Xuelong. Hyperspectral band selection via optimal neighborhood reconstruction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(12): 8465-8476.
[20] WANG Qi, LI Qiang, LI Xuelong. Hyperspectral band selection via adaptive subspace partition strategy[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(12): 4940-4950.
[21] SUN He, REN Jinchang, ZHAO Huimin, et al. Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection[J]. IEEE Transactions on Cybernetics, 2022, 52(1): 215-227.
[22] WANG Qi, LI Qiang, LI Xuelong. A fast neighborhood grouping method for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(6): 5028-5039.
[23] CAI Yaoming, LIU Xiaobo, CAI Zhihua. BS-nets: an end-to-end framework for band selection of hyperspectral image[J]. IEEE Tran-sactions on Geoscience and Remote Sensing, 2020, 58(3): 1969-1984.
[24] TSCHANNERL J, REN Jinchang, ZABALZA J, et al. Segmented autoencoders for unsupervised embedded hyperspectral band selection[C]//Proceedings of the 7th European Workshop on Visual Information Processing. Tampere:IEEE, 2018: 1-6.
[25] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496.
[26] SUN Kang, GENG Xiurui, JI Luyan. Exemplar component analysis: a fast band selection method for hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 998-1002.
[27] JIA Sen, TANG Guihua, ZHU Jiasong, et al. A novel ranking-based clustering approach for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 88-102.
[28] LUO Xiaoyan, XUE Rui, YIN Jihao. Information-assisted density peak index for hyperspectral band selection[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1870-1874.
[29] LI Qiang, WANG Qi, LI Xuelong. An efficient clustering method for hyperspectral optimal band selection via shared nearest neighbor[J]. Remote Sensing, 2019, 11(3): 350.
[30] YANG Rongchao, KAN Jiangming. An unsupervised hyperspectral band selection method based on shared nearest neighbor and correlation analysis[J]. IEEE Access, 2019, 7: 185532-185542.
[31] GUO Zhishuai, HUANG Tianyi, CAI Zhiling, et al. A new local density for density peak clustering[C]//Proceedings of 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining.Cham: Springer,2018: 426-438.
[32] KORN F, MUTHUKRISHNAN S. Influence sets based on reverse nearest neighbor queries[J]. ACM Sigmod Record, 2000, 29(2): 201-212.
[33] JARVIS R A, PATRICK E A. Clustering using a similarity measure based on shared near neighbors[J]. IEEE Transactions on Computers, 1973, 100(11): 1025-1034.
[34] ERTOZ L, STEINBACH M, KUMAR V. A new shared nearest neighbor clustering algorithm and its applications[C]//Proceedings of 2002 Workshop on Clustering High Dimensional Data and Its Applications at 2nd SIAM International Conference on Data Mining.[S.l.]:IEEE,2002.
[35] VAZQUEZ-FERNANDEZ E, DACAL-NIETO A, MARTIN F, et al. Entropy of gabor filtering for image quality assessment[C]//Proceedings of 2010 International Conference Image Analysis and Recognition.Berlin: Springer,2010: 52-61.
[36] 赵海士, 路来君, 杨晨. 一种基于图像熵的密度峰值聚类波段选择方法[J]. 吉林大学学报(理学版), 2017, 55(2): 376-378. ZHAO Haishi, LU Laijun, YANG Chen. A method for density peaks clustering band selection based on image entropy[J]. Journal of Jilin University (Science Edition), 2017, 55(2): 376-378.

高光谱影像逆近邻密度峰值聚类的波段选择算法

Band selection algorithm for reverse nearest neighbor density peak clustering of hyperspectral images

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