Kernel Tensor Sparse Coding Model for Precise Crop Classification of UAV Hyperspectral Image

In this letter, a kernel tensor sparse coding model (KTSCM) is proposed for precise crop classification of unmanned aerial vehicle (UAV) hyperspectral image (HSI). Benefited from the kernel tensor representation mechanism in KTSCM, which can not only improve the linear separation but also well prese...

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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 20; S. 1
Hauptverfasser: Yang, Lixia, Zhang, Rui, Bao, Yajun, Yang, Shuyuan, Jiao, Licheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Autonomous aerial vehicles
Classification
Coding
Computation
Cost analysis
Crops
Dictionaries
Encoding
Exact solutions
Hyperspectral imaging
Image classification
Kernel
Kernel tensor sparse coding
Kernels
Mathematical analysis
precise crop classification
Tensors
unmanned aerial vehicle hyperspectral image
Unmanned aerial vehicles
ISSN:1545-598X, 1558-0571
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Zusammenfassung:In this letter, a kernel tensor sparse coding model (KTSCM) is proposed for precise crop classification of unmanned aerial vehicle (UAV) hyperspectral image (HSI). Benefited from the kernel tensor representation mechanism in KTSCM, which can not only improve the linear separation but also well preserving the spatial-spectral structures of land-covers, the discriminability of UAV HSI is greatly improved. The L1-norm based tensor sparsity makes the tensor operation in KTSCM can be equivalently converted to matrix operation, which greatly reduces the computation cost. Furthermore, the analytical solution to KTSCM allows it be well optimized with very few iterations. The performance of KTSCM is assessed on two real UAV HSIs. The experimental results indicate that KTSCM can provides rapid and accurate crop classification results with limited labeled pixels and outperforms the related counterparts.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3326452