MULTI-SCALE FEATURE FUSION FOR HYPERSPECTRAL AND LIDAR DATA JOINT CLASSIFICATION

被引：5

作者：

Zhang, Maqun ^{[1
,2
]}

Gao, Feng ^{[1
,2
]}

Dong, Junyu ^{[1
,2
]}

Qi, Lin ^{[1
,2
]}

机构：

[1] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China

[2] Ocean Univ China, Inst Marine Dev, Qingdao, Peoples R China

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

关键词：

hyperspectral image; LiDAR; cross-modal data fusion; classification;

D O I：

10.1109/IGARSS46834.2022.9884168

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

To exploit the multi-scale information to improve the feature representation, we propose a multi-scale feature fusion network for hyperspectral image (HSI) and LiDAR data joint classification. The model is comprised of three parts: feature extraction module, feature fusion module, and MSF(Multi-Scale Fusion) module. The feature fusion module integrates the multi-modal information into the two inputs by fusing attention. MSF module integrates richer semantic information by integrating multi-scale information to improve performance. Experimental results show that the proposed method is effective in multi-modal data joint classification.

引用

页码：2856 / 2859

页数：4

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