TY - GEN
T1 - WATER RETRIEVAL EMBEDDED DEEP NETWORK FOR HYPERSPECTRAL IMAGE REFINED CLASSIFICATION
AU - Liang, Xuejian
AU - Zhang, Ye
AU - Zhang, Junping
AU - Miao, Xinyuan
AU - Zhou, Xinyu
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Hyperspectral image (HSI) classification methods based on deep learning (DL) algorithms have achieved significant improvements on abundant samples. However, due to the limitation of practically available samples, the difficulty of representative feature extraction from small-sized samples and the loss of subtle diagnostic features in DL iteration results in the accuracy reduction of interclass and intraclass in refined classification, respectively. To address these issues, a water retrieval embedded deep network is proposed in this paper. The relative water content retrieval (RWCR) of the proposed network is embedded as a subnet, which is responsible for extracting subtle diagnostic features of relative water content (RWC) to enhance the representation of features in classification. The experimental results verify the effectiveness of RWCR for improving the interclass and intraclass accuracy in refined classification. Moreover, the superiority of the proposed network is also demonstrated in comparison with state-of-the-art methods.
AB - Hyperspectral image (HSI) classification methods based on deep learning (DL) algorithms have achieved significant improvements on abundant samples. However, due to the limitation of practically available samples, the difficulty of representative feature extraction from small-sized samples and the loss of subtle diagnostic features in DL iteration results in the accuracy reduction of interclass and intraclass in refined classification, respectively. To address these issues, a water retrieval embedded deep network is proposed in this paper. The relative water content retrieval (RWCR) of the proposed network is embedded as a subnet, which is responsible for extracting subtle diagnostic features of relative water content (RWC) to enhance the representation of features in classification. The experimental results verify the effectiveness of RWCR for improving the interclass and intraclass accuracy in refined classification. Moreover, the superiority of the proposed network is also demonstrated in comparison with state-of-the-art methods.
KW - Deep learning
KW - Feature extraction
KW - Hyperspectral image refined classification
KW - Relative water content retrieval
UR - https://www.scopus.com/pages/publications/85126068122
U2 - 10.1109/IGARSS47720.2021.9555173
DO - 10.1109/IGARSS47720.2021.9555173
M3 - 会议稿件
AN - SCOPUS:85126068122
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2242
EP - 2245
BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Y2 - 12 July 2021 through 16 July 2021
ER -