Spatial-Spectral Bipartite Graph Clustering With Low-Frequency Tensor Regularization for Hyperspectral and LiDAR Data

The increasing complexity of remote sensing (RS) applications necessitates multimodal data fusion to overcome the inherent limitations of single-source data. In particular, the integration of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data captures complementary spectral and...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 63; S. 1 - 17
Hauptverfasser:	Cao, Zhe, Lu, Yihang, Xin, Haonan, Yu, Chuanqiang, Wang, Rong, Nie, Feiping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Annotations Band selection Bipartite graph Clustering Clustering algorithms Complexity Computational efficiency Data integration Graph theory Hyperspectral imaging Information processing Laser radar Lidar low-frequency tensor nuclear norm (LFTNN) multimodal remote sensing (RS) Multisensor fusion Regularization Remote sensing Scene analysis spatial bipartite graph Spatial data Surface topography Synthetic aperture radar Tensors Unsupervised learning
ISSN:	0196-2892, 1558-0644
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Abstract	The increasing complexity of remote sensing (RS) applications necessitates multimodal data fusion to overcome the inherent limitations of single-source data. In particular, the integration of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data captures complementary spectral and spatial features, significantly enhancing object discrimination. Unlike supervised methods requiring costly expert annotations, unsupervised clustering eliminates labeling needs, thereby offering an efficient solution for complex scene analysis with reduced deployment costs. However, existing unsupervised clustering methods still face several challenges, including insufficient utilization of spatial information, high data dimensionality, and interference from high-frequency noise, which collectively hinder their further development and practical application. To address these issues, this article proposes a novel method called spatial-spectral bipartite graph clustering with low-frequency tensor (SSBC-LFT) regularization for hyperspectral and LiDAR data. The proposed method first focuses on modeling clustering structures, innovatively constructing spatially informed bipartite graphs for each modality to enrich spatial details while reducing computational complexity. Additionally, an adaptive spectral band selection mechanism dynamically assigns higher weights to discriminative bands during clustering while suppressing redundant ones. Finally, through proposed low-frequency tensor nuclear norm (LFTNN) regularization, high-frequency noise is effectively filtered in the frequency domain, extracting stable and consistent cross-modal structural information from the low-rank space to further enhance clustering robustness and accuracy. Extensive experiments on real-world multimodal RS datasets demonstrate the superior performance of the SSBC-LFT model, offering a robust and effective solution for unsupervised clustering in complex scenarios.
AbstractList	The increasing complexity of remote sensing (RS) applications necessitates multimodal data fusion to overcome the inherent limitations of single-source data. In particular, the integration of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data captures complementary spectral and spatial features, significantly enhancing object discrimination. Unlike supervised methods requiring costly expert annotations, unsupervised clustering eliminates labeling needs, thereby offering an efficient solution for complex scene analysis with reduced deployment costs. However, existing unsupervised clustering methods still face several challenges, including insufficient utilization of spatial information, high data dimensionality, and interference from high-frequency noise, which collectively hinder their further development and practical application. To address these issues, this article proposes a novel method called spatial–spectral bipartite graph clustering with low-frequency tensor (SSBC-LFT) regularization for hyperspectral and LiDAR data. The proposed method first focuses on modeling clustering structures, innovatively constructing spatially informed bipartite graphs for each modality to enrich spatial details while reducing computational complexity. Additionally, an adaptive spectral band selection mechanism dynamically assigns higher weights to discriminative bands during clustering while suppressing redundant ones. Finally, through proposed low-frequency tensor nuclear norm (LFTNN) regularization, high-frequency noise is effectively filtered in the frequency domain, extracting stable and consistent cross-modal structural information from the low-rank space to further enhance clustering robustness and accuracy. Extensive experiments on real-world multimodal RS datasets demonstrate the superior performance of the SSBC-LFT model, offering a robust and effective solution for unsupervised clustering in complex scenarios.
Author	Lu, Yihang Wang, Rong Xin, Haonan Cao, Zhe Yu, Chuanqiang Nie, Feiping
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SubjectTerms	Accuracy Annotations Band selection Bipartite graph Clustering Clustering algorithms Complexity Computational efficiency Data integration Graph theory Hyperspectral imaging Information processing Laser radar Lidar low-frequency tensor nuclear norm (LFTNN) multimodal remote sensing (RS) Multisensor fusion Regularization Remote sensing Scene analysis spatial bipartite graph Spatial data Surface topography Synthetic aperture radar Tensors Unsupervised learning
Title	Spatial-Spectral Bipartite Graph Clustering With Low-Frequency Tensor Regularization for Hyperspectral and LiDAR Data
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