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Stacked Tensor Subspace Learning for hyperspectral image classification
Wei Y.2; Zhou Y.1
Conference NameInternational Joint Conference on Neural Networks (IJCNN)
Source PublicationProceedings of the International Joint Conference on Neural Networks
Conference DateJUL 24-29, 2016
Conference PlaceVancouver, CANADA

In this paper, we present a hierarchical feature learning method called Stacked Tensor Subspace Learning (STSL). It can jointly learn spectral and spatial features of hyperspectral images (HSIs) by iteratively abstracting neighboring regions. STSL is able to learn discriminative spectral-spatial features of the input HSI at different scales. In STSL, the joint spectral and spatial features are extracted using Marginal Fisher Analysis (MFA) and Tensor Principal Component Analysis (TPCA). Then Kernel-based Extreme Learning Machine (KELM), a shallow neural network, is embedded in the proposed method to classify image pixels. The important contributions to the success of STSL are exploiting local spatial structure of HSI by using tensor method and designing hierarchical architecture. Extensive experimental results on two challenging HSI data sets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show that the proposed method can produce good classification accuracy with smaller training sets.

URLView the original
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS IDWOS:000399925502024
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.Universidade de Macau
2.Huazhong Normal University
Recommended Citation
GB/T 7714
Wei Y.,Zhou Y.. Stacked Tensor Subspace Learning for hyperspectral image classification[C],2016:1985-1992.
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