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
Online-Zugang:	Volltext
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