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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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 63; s. 1 - 17
Hlavní autori:	Cao, Zhe, Lu, Yihang, Xin, Haonan, Yu, Chuanqiang, Wang, Rong, Nie, Feiping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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