TY - GEN
T1 - Multi-Source Remote Sensing Data Cross Scene Classification Based on Multi-Graph Matching
AU - Zhang, Mengmeng
AU - Zhao, Xudong
AU - Li, Wei
AU - Zhang, Yuxiang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Multi-source joint classification has been extensively investigated in single scenario setting; however, for cross scene (CS) classification, few studies have been conducted for evaluating the collaborative performance of multi-sources. In this paper, using hyperspectral image (HSI) and light detection and ranging (LiDAR) data, we propose a multi-source CS classification method, and build source-related alignment to reduce statistical shift. Both geometrical and statistical alignments are considered to learn common-subspaces of each source with preserving discrimination information. Finally, the aligned features from both sources are integrated for final classification. Experimental results demonstrate the superior of the proposed method over other state-of-the-art CS approaches.
AB - Multi-source joint classification has been extensively investigated in single scenario setting; however, for cross scene (CS) classification, few studies have been conducted for evaluating the collaborative performance of multi-sources. In this paper, using hyperspectral image (HSI) and light detection and ranging (LiDAR) data, we propose a multi-source CS classification method, and build source-related alignment to reduce statistical shift. Both geometrical and statistical alignments are considered to learn common-subspaces of each source with preserving discrimination information. Finally, the aligned features from both sources are integrated for final classification. Experimental results demonstrate the superior of the proposed method over other state-of-the-art CS approaches.
KW - Deep learning
KW - cross scene
KW - distribution alignment
KW - joint classification
UR - https://www.scopus.com/pages/publications/85140412213
U2 - 10.1109/IGARSS46834.2022.9884311
DO - 10.1109/IGARSS46834.2022.9884311
M3 - Conference contribution
AN - SCOPUS:85140412213
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 827
EP - 830
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -