TY - GEN
T1 - Main direction - An effective statistic for hyperspectral image classification
AU - Han, Manli
AU - Feng, Yan
AU - Xu, Chao
AU - Sun, Jinazuo
AU - Wang, Ming
AU - Mei, Shaohui
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In the hyperspectral image classification literature, mean and covariance are prominently used statistics. The first order statistics, i.e., mean, although easily estimated, can have low discriminative property. However, the discrimination power of the second order statistic, i.e., covariance, is strong. The downside of the estimation of covariance is the large sample size. In this paper, we propose a effective statistic named main direction. The main direction is defined as a direction in which the variation or the distance of the samples becomes the largest. Besides, the estimation of the main direction is stable even when the size of the sample set is very small. Based on the main direction, we propose a modified spectral angle mapper classifier and demonstrate the effectiveness of the modified classifier with experimental results.
AB - In the hyperspectral image classification literature, mean and covariance are prominently used statistics. The first order statistics, i.e., mean, although easily estimated, can have low discriminative property. However, the discrimination power of the second order statistic, i.e., covariance, is strong. The downside of the estimation of covariance is the large sample size. In this paper, we propose a effective statistic named main direction. The main direction is defined as a direction in which the variation or the distance of the samples becomes the largest. Besides, the estimation of the main direction is stable even when the size of the sample set is very small. Based on the main direction, we propose a modified spectral angle mapper classifier and demonstrate the effectiveness of the modified classifier with experimental results.
KW - Classification
KW - Hyperspectral images
KW - Main direction
KW - Spectral angle mapper
UR - http://www.scopus.com/inward/record.url?scp=85064956101&partnerID=8YFLogxK
U2 - 10.1109/FSKD.2018.8686960
DO - 10.1109/FSKD.2018.8686960
M3 - 会议稿件
AN - SCOPUS:85064956101
T3 - ICNC-FSKD 2018 - 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
SP - 283
EP - 288
BT - ICNC-FSKD 2018 - 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
A2 - Xiao, Zheng
A2 - Wang, Lipo
A2 - Xiao, Guoqing
A2 - Ning, Xiong
A2 - Li, Kenli
A2 - Li, Maozhen
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018
Y2 - 28 July 2018 through 30 July 2018
ER -