Hyperspectral Image Classification by Genetic Relevance Vector Machine

Abstract

Abstract: The adjacent bands of hyperspectral image are highly correlated. It is not optimum to classify the hyperspectral image in the high dimensional space. In addition, optimizing the parameter of classifier by the cross validation method is not a trivial task. Aiming at the two targets, the classification of the hyperspectral image with genetic relevance vector machine (GA-RVM) was proposed. GARVM searches the best parameter and feature space for relevance vector machine (RVM), to reduce the redundant information and simplify the parameter optimization procedure. GA-RVM was evaluated by several experiments. Nearly 50% of the bands are eliminated during the optimization, leading to a 3% increase in the overall accuracy. The improvements are obvious for the hard-to-separate classes. Two kinds of soybeans that have the most misclassifications acquire an 8% improvement in accuracy.

Key words: hyperspectral, classification, relevance vector machine, genetic algorithm

CLC Number:

TP 751.1

DONG Chao-1, 2 , TIAN Lian-Fang-1, ZHAO Hui-Jie-2. Hyperspectral Image Classification by Genetic Relevance Vector Machine[J]. Journal of Shanghai Jiaotong University, 2011, 45(10): 1516-1520.

References

［1］Hughes G F. On the mean accuracy of statistical pattern recognizers［J］. IEEE Transactions on Information Theory, 1968, 14(1): 5563.

［2］Tadjudin S, Landgrebe D A. Covariance estimation with limited training samples［J］. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(4): 21132118.

［3］Kuo B C, Landgrebe D A. Nonparametric weighted feature extraction for classification［J］. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(5): 10961105.

［4］Dundar M M, Landgrebe D A. A costeffective semisupervised classifier approach with kernels［J］. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(1): 264270.

［5］Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines［J］. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 17781790.

［6］Bazi Y, Melgani F. Toward an optimal SVM classification system for hyperspectral remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(11): 33743385.

［7］Tipping M E. Sparse bayesian learning and the relevance vector machine［J］. Journal of Machine Learning Research, 2001, 1: 211244.

［8］Demir B, Erturk S. Hyperspectral image classification using relevance vector machines［J］. IEEE GeoScience and Remote Sensing Letters, 2007, 4(4): 586590.

［9］Foody G M. RVMbased multiclass classification of remotely sensed data［J］. International Journal of Remote Sensing, 2008, 29(6): 18171823.

［10］CampsValls G, GomezChova L, MunozMari J, et al. Retrieval of oceanic chlorophyll concentration with relevance vector machines［J］. Remote Sensing of Environment, 2006, 105: 2333.

［11］Landgrebe D A. AVIRIS NW. Indiana’s Indian pines data set［DB/OL］. (19920603)［19960506］. ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C, 1992.

[1]	NI Yangfan, YANG Yuanyuan, XIE Zhe, ZHENG Dezhong, WANG Weidong. Multi-Feature Extraction of Pulmonary Nodules Based on LSTM and Attention Structure [J]. Journal of Shanghai Jiao Tong University, 2022, 56(8): 1078-1088.
[2]	YAN Qing, LU Jiansha, JIANG Weiguang, SHAO Yiping, TANG Hongtao, LI Yingde. Path Optimization of Stacker in Compact Storage System with Dual-Port Layout [J]. Journal of Shanghai Jiao Tong University, 2022, 56(7): 858-867.
[3]	WU Caiyu, (吴彩钰), SABOR Nabil, ZHOU Shihong, (周世鸿), WANG Min, (王敏), YING Liang (应亮), WANG Guoxing∗ (王国兴). Gram Matrix-Based Convolutional Neural Network for Biometric Identification Using Photoplethysmography Signal [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 463-472.
[4]	ZHOU Tianyan, FENG Xiaoen, FAN Yunfeng, DONG Shiyin, LI Yuqing, JIN Huizhong. Optimization Model of Ground Air Defense Force Deployment to Avoid Excessive Air Defense Firepower [J]. Air & Space Defense, 2022, 5(4): 19-23.
[5]	WANG Xiaojian, HONG Jun, CHEN Jinghua, LI Hongguang. Weight Reduction Study of Box Structure Based on Parametric Modeling and Response Surface Optimization [J]. Air & Space Defense, 2022, 5(4): 60-66.
[6]	LIU Jiea (刘洁), ZHANG Baojib∗ (张宝吉). Multiobjective Optimization of Hull Form Based on Global Optimization Algorithm [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(3): 346-355.
[7]	WANG Zhuoxin, ZHAO Haitao, XIE Yuehan, REN Hantao, YUAN Mingqing, ZHANG Boming, CHEN Ji’an. Prediction of Modulus of Composite Materials by BP Neural Network Optimized by Genetic Algorithm [J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1341-1348.
[8]	ZHOU Yutai, XU Yue, LI Yu, JIANG Guotao. Genetic Algorithm Based Method of Optimal Deployment for Three-Dimensional Radar Networks Under Jamming Situation [J]. Air & Space Defense, 2022, 5(1): 52-59.
[9]	TUNG Hao (董昊), ZHENG Chao (郑超), MAO Xinsheng(毛新生), QIAN Dahong (钱大宏). Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(1): 55-69.
[10]	LI Cuiming, WANG Ning, ZHANG Chen. Hierarchical Mission Planning for Cleaning Photovoltaic Panels Based on Improved Genetic Algorithm [J]. Journal of Shanghai Jiao Tong University, 2021, 55(9): 1169-1174.
[11]	LUO Huimiao (罗晖淼), ZHAO Dongbiao∗ (赵东标), FU Wenqiang (付文强). Speed Planning Algorithm Based on Improved S-Type Acceleration and Deceleration Model [J]. J Shanghai Jiaotong Univ Sci, 2021, 26(6): 786-793.
[12]	HE Xinlin, QI Zongfeng, LI Jianxun. Unbalanced Learning of Generative Adversarial Network Based on Latent Posterior [J]. Journal of Shanghai Jiao Tong University, 2021, 55(5): 557-565.
[13]	JIN Yudong (靳宇栋), FENG Jiabo (冯家波), ZHANG Weijun (张伟军). UAV Task Allocation for Hierarchical Multiobjective Optimization in Complex Conditions Using Modified NSGA-III with Segmented Encoding [J]. J Shanghai Jiaotong Univ Sci, 2021, 26(4): 431-445.
[14]	GU Yifan, ZHAO Wenlong, TANG Shanjun, YANG Qingyu, ZHENG Xin. Research on Target Spatial Collaborative Positioning Methods for Distributed Active/Passive Imaging Detection System [J]. Air & Space Defense, 2021, 4(4): 119-126.
[15]	LI Fen, ZHOU Erchang, SUN Gaiping, BAI Yongqing, TONG Li, LIU Bangyin, ZHAO Jinbin. A Novel Weather Classification Method and Its Application in Photovoltaic Power Prediction [J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1510-1519.