TY - GEN
T1 - Hyperspectral band selection with convolutional neural network
AU - Cai, Rui
AU - Yuan, Yuan
AU - Lu, Xiaoqiang
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Band selection
KW - Convolutional neural network
KW - Feature extraction
KW - Hyperspectral image classification
UR - http://www.scopus.com/inward/record.url?scp=85057209090&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-03341-5_33
DO - 10.1007/978-3-030-03341-5_33
M3 - 会议稿件
AN - SCOPUS:85057209090
SN - 9783030033408
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 396
EP - 408
BT - Pattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings
A2 - Chen, Xilin
A2 - Lai, Jian-Huang
A2 - Zheng, Nanning
A2 - Liu, Cheng-Lin
A2 - Tan, Tieniu
A2 - Zhou, Jie
A2 - Zha, Hongbin
PB - Springer Verlag
T2 - 1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018
Y2 - 23 November 2018 through 26 November 2018
ER -