TY - GEN
T1 - Hyperspectral image classification with small training set by deep network and relative distance prior
AU - Ma, Xiaorui
AU - Wang, Hongyu
AU - Geng, Jie
AU - Wang, Jie
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - This paper presents a hyperspectral image classification method based on deep network, which has shown great potential in various machine learning tasks. Since the quantity of training samples is the primary restriction of the performance of classification methods, we impose a new prior on the deep network to deal with the instability of parameter estimation under this circumstances. On the one hand, the proposed method adjusts parameters of the whole network to minimize the classification error as all supervised deep learning algorithm, on the other hand, unlike others, it also minimize the discrepancy within each class and maximize the difference between different classes. The experimental results showed that the proposed method is able to achieve great performance under small training set.
AB - This paper presents a hyperspectral image classification method based on deep network, which has shown great potential in various machine learning tasks. Since the quantity of training samples is the primary restriction of the performance of classification methods, we impose a new prior on the deep network to deal with the instability of parameter estimation under this circumstances. On the one hand, the proposed method adjusts parameters of the whole network to minimize the classification error as all supervised deep learning algorithm, on the other hand, unlike others, it also minimize the discrepancy within each class and maximize the difference between different classes. The experimental results showed that the proposed method is able to achieve great performance under small training set.
KW - Deep learning
KW - Hyperspectral image
KW - Supervised classification
UR - https://www.scopus.com/pages/publications/85007508508
U2 - 10.1109/IGARSS.2016.7729849
DO - 10.1109/IGARSS.2016.7729849
M3 - 会议稿件
AN - SCOPUS:85007508508
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3282
EP - 3285
BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Y2 - 10 July 2016 through 15 July 2016
ER -