Bridging HSI and LiDAR Data With Frequency-Domain Hierarchical Fusion for Enhanced Classification

doi:10.1109/TGRS.2025.3589080

	Bridging HSI and LiDAR Data With Frequency-Domain Hierarchical Fusion for Enhanced Classification
	Gong, Luqi 1,2; Bai, Rui 3; Li, Yilang 4; Chen, Yue 1; Fan, Fanda 5; Zhao, Shuai 2; Li, Chao 1
	2025
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	63 页码:18
摘要	Remote sensing data from hyperspectral imaging (HSI) and light detection and ranging (LiDAR) provide complementary perspectives for terrain and object analysis. However, existing methods for multimodal data fusion primarily focus on spatial-domain feature alignment, often overlooking the potential of frequency-domain information to enhance classification accuracy. To bridge this gap, we introduce the frequency-domain hierarchical perception fusion network (FHPF-Net), a novel framework for precise classification of remote sensing images. This network leverages both spatial-domain information and frequency-domain information and provides a new perspective for heterogeneous data integration. To extract and utilize frequency-domain features, we propose the high-to-low spectral separation and mining (HLSSM) module, which isolates high-frequency details such as edges and textures from low-frequency structural patterns in HSI and LiDAR data. This separation facilitates targeted feature extraction (FE) while preserving crucial contextual information. Additionally, we introduce the hierarchical superimposed multidomain information fusion (HSMIF) module, which employs a multilevel fusion strategy to integrate spatial- and frequency-domain features, ensuring consistency and complementarity between the two data sources. Finally, we introduce a learnable voting pre-label fusion (LVPF) strategy to effectively integrate multibranch outputs, enhancing classification performance and model robustness. The proposed FHPF-Net effectively captures diverse responses across heterogeneous data types, enabling robust classification in complex environments. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods.
关键词	Frequency-domain analysis Laser radar Feature extraction Data mining Phase frequency detectors Remote sensing Principal component analysis Soft sensors Fast Fourier transforms Accuracy Category-aware frequency domain hyperspectral imaging (HSI) light detection and ranging (LiDAR) image
DOI	10.1109/TGRS.2025.3589080
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62472043] ; National Natural Science Foundation of China[U21A20468] ; National Key Research and Development Program of China[2022YFB4501600]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001582016000008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41647
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Shuai; Li, Chao
作者单位	1.Zhejiang Lab, Res Ctr Space Comp Syst, Hangzhou 311121, Peoples R China 2.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China 3.Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China 4.Changsha Univ Sci & Technol, Sch Phys & Elect, Changsha 410004, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Gong, Luqi,Bai, Rui,Li, Yilang,et al. Bridging HSI and LiDAR Data With Frequency-Domain Hierarchical Fusion for Enhanced Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:18.
APA	Gong, Luqi.,Bai, Rui.,Li, Yilang.,Chen, Yue.,Fan, Fanda.,...&Li, Chao.(2025).Bridging HSI and LiDAR Data With Frequency-Domain Hierarchical Fusion for Enhanced Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,18.
MLA	Gong, Luqi,et al."Bridging HSI and LiDAR Data With Frequency-Domain Hierarchical Fusion for Enhanced Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):18.