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Hyperspectral band selection with convolutional neural network
Cai, Rui1,2; Yuan, Yuan1; Lu, Xiaoqiang1
2018
Conference Name1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018
Source PublicationPattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings
Volume11259 LNCS
Pages396-408
Conference Date2018-11-23
Conference PlaceGuangzhou, China
PublisherSpringer Verlag
Contribution Rank1
Abstract

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.

Department光学影像学习与分析中心
DOI10.1007/978-3-030-03341-5_33
Indexed ByEI
ISBN9783030033408
Language英语
ISSN03029743;16113349
EI Accession Number20184806152364
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30862
Collection光学影像学习与分析中心
Corresponding AuthorLu, Xiaoqiang
Affiliation1.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
Recommended Citation
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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