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Hyperspectral band selection with convolutional neural network
Cai, Rui1,2; Yuan, Yuan1; Lu, Xiaoqiang1
2018
会议名称1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018
会议录名称Pattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings
卷号11259 LNCS
页码396-408
会议日期2018-11-23
会议地点Guangzhou, China
出版者Springer Verlag
产权排序1
摘要

Band selection is a kind of dimension reduction method, which tries to remove redundant bands and choose several pivotal bands to represent the entire hyperspectral image (HSI). Supervised band selection algorithms tend to perform well because of the introduction of prior information. However, The traditional methods are based on the entire image, without taking into account the differences in ground categories, and cannot figure out which band is discriminative for a specific category. In this paper, a supervised method is proposed based on the ground category with convolutional neural network (CNN). Firstly, we propose a structure called contribution map which can record discriminative feature location. Secondly, the contribution map is added to CNN to generate a new model called contribution map based CNN (CM-CNN). Thirdly, we apply CM-CNN for HSI classification with the whole bands. Then, we can get the contribution map which records discriminative bands location for each category. Finally, the contribution map guides us to select discriminative bands. We found that CM-CNN model can obtain a satisfactory classification result while preserving the position information of important bands. To verify the superiority of the proposed method, experiments are conducted on HSI classification. The results demonstrated the reliability of the proposed method in HSI classification. ? Springer Nature Switzerland AG 2018.

作者部门光学影像学习与分析中心
DOI10.1007/978-3-030-03341-5_33
收录类别EI
ISBN号9783030033408
语种英语
ISSN号03029743;16113349
EI入藏号20184806152364
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/30862
专题光学影像学习与分析中心
通讯作者Lu, Xiaoqiang
作者单位1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an; Shaanxi; 710119, China;
2.University of Chinese Academy of Sciences, 19A Yuquanlu, Beijing; 100049, China
推荐引用方式
GB/T 7714
Cai, Rui,Yuan, Yuan,Lu, Xiaoqiang. Hyperspectral band selection with convolutional neural network[C]:Springer Verlag,2018:396-408.
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