TY - GEN
T1 - Multisource Remote Sensing Data Classification Using Fractional Fourier Transformer
AU - Zhao, Xudong
AU - Zhang, Mengmeng
AU - Tao, Ran
AU - Li, Wei
AU - Liao, Wenzhi
AU - Phlips, Wilfried
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Focusing on joint classification of Hyperspectral image (HSI) and Light detection and ranging (LiDAR) data, a fractional Fourier image transformer (FrIT) is proposed as a backbone network in this paper. In the proposed FrIT, HSI and LiDAR data are firstly fused at pixel-level. Both multi-source and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contexts and sequential feature extraction. Unlike the attention-based representations in classic visual image transformer (VIT), FrIT is capable of speeding up the transformer architectures massively. To reduce the information loss from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. At last, to evaluate the performance of FrIT, a new HSI and LiDAR benchmark is provided for extensive experiments, on which the proposed FrIT gains an improvement of 3% over state-of-the-art methods.
AB - Focusing on joint classification of Hyperspectral image (HSI) and Light detection and ranging (LiDAR) data, a fractional Fourier image transformer (FrIT) is proposed as a backbone network in this paper. In the proposed FrIT, HSI and LiDAR data are firstly fused at pixel-level. Both multi-source and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contexts and sequential feature extraction. Unlike the attention-based representations in classic visual image transformer (VIT), FrIT is capable of speeding up the transformer architectures massively. To reduce the information loss from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. At last, to evaluate the performance of FrIT, a new HSI and LiDAR benchmark is provided for extensive experiments, on which the proposed FrIT gains an improvement of 3% over state-of-the-art methods.
KW - Fractional Fourier image transformer (FrIT)
KW - hyperspectral image (HSI)
KW - light detection and ranging (LiDAR)
KW - multisource remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85140355869&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9884573
DO - 10.1109/IGARSS46834.2022.9884573
M3 - Conference contribution
AN - SCOPUS:85140355869
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 823
EP - 826
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
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 -