Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification

Most hyperspectral image (HSI) data exist in the form of tensor; the tensor representation preserves the potential spatial-spectral structure information compared with the vector representation, which can help improve the classification performance of HSI. In this article, a deep high-order tensor c...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 11
Hlavní autoři: Cheng, Chunbo, Li, Hong, Peng, Jiangtao, Cui, Wenjing, Zhang, Liming
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Classification
Convolution
Convolutional codes
Deep high-order tensor convolutional sparse coding (CSC)
deep learning
Feature extraction
graph-based learning (GSL)
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Image coding
Kernel
Mathematical analysis
Methods
Representations
Spectra
Tensors
ISSN:0196-2892, 1558-0644
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Most hyperspectral image (HSI) data exist in the form of tensor; the tensor representation preserves the potential spatial-spectral structure information compared with the vector representation, which can help improve the classification performance of HSI. In this article, a deep high-order tensor convolutional sparse coding (CSC) model is proposed, which can be used to train deep high-order filters. Based on the deep high-order tensor CSC model, a deep feature extraction network (DHTCSCNet) is constructed, which is used for feature extraction of HSIs. By combining the spectral-spatial feature and the features extracted by the proposed DHTCSCNet at each layer, a combined feature that incorporates shallow, deep, spectral, and spatial features can be obtained. Then, the graph-based learning (GSL) methods are used to classify the combined feature. Experimental results show that the DHTCSCNet can obtain better classification performance compared with other HSI classification methods.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3134682