TY - GEN
T1 - Multisource Cross-Scene Classification Using Fractional Fusion and Spatial-Spectral Domain Adaptation
AU - Zhao, Xudong
AU - Zhang, Mengmeng
AU - Tao, Ran
AU - Li, Wei
AU - Liao, Wenzhi
AU - Philips, Wilfried
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - To solve the limitation of labeled samples in hyperspectral image (HSI) classification, cross-scene learning methods are developed recently. However, the disparity caused by environmental variation between HSI scenes is still a challenge. As a supplement, light detection and ranging (LiDAR) data provides elevation and spatial information regardless the variations. In this paper, we propose a multisource cross-scene classification method using fractional fusion and spatial-spectral domain adaptation to reduce disparity between scenes. The spatial information of HSI is preserved by fractional differential masks (FrDM) firstly. Then the LiDAR data is utilized for spectral alignment of HSI. The utilization of LiDAR data reduces the pixel-level disparity between scenes. At last, a spatial-spectral domain adaptation network is proposed for feature extraction and classification. Experimental results on HSI and LiDAR scenes show 5% improvements in overall accuracy compared with state-of-the-art methods.
AB - To solve the limitation of labeled samples in hyperspectral image (HSI) classification, cross-scene learning methods are developed recently. However, the disparity caused by environmental variation between HSI scenes is still a challenge. As a supplement, light detection and ranging (LiDAR) data provides elevation and spatial information regardless the variations. In this paper, we propose a multisource cross-scene classification method using fractional fusion and spatial-spectral domain adaptation to reduce disparity between scenes. The spatial information of HSI is preserved by fractional differential masks (FrDM) firstly. Then the LiDAR data is utilized for spectral alignment of HSI. The utilization of LiDAR data reduces the pixel-level disparity between scenes. At last, a spatial-spectral domain adaptation network is proposed for feature extraction and classification. Experimental results on HSI and LiDAR scenes show 5% improvements in overall accuracy compared with state-of-the-art methods.
KW - Fractional fusion (FrF)
KW - cross-scene classification
KW - hyperspectral image (HSI)
KW - light detection and ranging (LiDAR)
KW - spatial-spectral domain adaptation (SSDA)
UR - http://www.scopus.com/inward/record.url?scp=85141894996&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9884659
DO - 10.1109/IGARSS46834.2022.9884659
M3 - Conference contribution
AN - SCOPUS:85141894996
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 699
EP - 702
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 -