TY - GEN
T1 - FEATURE EXCHANGE FOR MULTISOURCE DATA CLASSIFICATION IN WETLAND SCENE
AU - Gao, Yunhao
AU - Li, Wei
AU - Zhang, Mengmeng
AU - Tao, Ran
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Wetland classification is of great significance for monitoring. Recently, collaborative analysis of multisource data has received special attention considering the limitations of single source data. In this paper, a wetland classification method based on feature exchange is proposed. Firstly, the weighting shared residual blocks are utilized for feature extraction. Then, the scaling factors in batch normalization (BN) self-determine the redundancy of current channel, which is replaced by another channel when the scaling factor is less than the threshold. To eliminate unnecessary channels and improve the generalization, sparsity constraint is employed on partial scaling factors. Experimental results on multisource wetland dataset demonstrate that the proposed method outperforms other competitive works.
AB - Wetland classification is of great significance for monitoring. Recently, collaborative analysis of multisource data has received special attention considering the limitations of single source data. In this paper, a wetland classification method based on feature exchange is proposed. Firstly, the weighting shared residual blocks are utilized for feature extraction. Then, the scaling factors in batch normalization (BN) self-determine the redundancy of current channel, which is replaced by another channel when the scaling factor is less than the threshold. To eliminate unnecessary channels and improve the generalization, sparsity constraint is employed on partial scaling factors. Experimental results on multisource wetland dataset demonstrate that the proposed method outperforms other competitive works.
KW - Feature exchange
KW - Multisource remote sensing data
KW - Wetland classification
UR - http://www.scopus.com/inward/record.url?scp=85126030346&partnerID=8YFLogxK
U2 - 10.1109/IGARSS47720.2021.9553840
DO - 10.1109/IGARSS47720.2021.9553840
M3 - Conference contribution
AN - SCOPUS:85126030346
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5382
EP - 5385
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 -