Hybrid Probabilistic Sparse Coding With Spatial Neighbor Tensor for Hyperspectral Imagery Classification

Under the umbrella of tensor algebra, this paper proposes a new sparse-coding-based classifier (SCC) for hyperspectral imagery classification (HIC). By utilizing the tensor forms of hyperspectral pixels, we advance a tensor sparse-coding model which preserves as many original spatial constraints of...

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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 56; číslo 5; s. 2491 - 2502
Hlavní autori: Yang, Lixia, Wang, Min, Yang, Shuyuan, Zhao, Hui, Jiao, Licheng, Feng, Xiangchu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Classification
Coding
Encoding
Hybrid probabilistic
hyperspectral imagery classification (HIC)
Hyperspectral imaging
Image classification
Imagery
Mathematical analysis
Maximization
Pixels
Probabilistic logic
Probability theory
Regularization
spatial constraint
State of the art
Statistical analysis
Tensile stress
tensor sparse coding
Tensors
Training
Uncertainty
ISSN:0196-2892, 1558-0644
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Shrnutí:Under the umbrella of tensor algebra, this paper proposes a new sparse-coding-based classifier (SCC) for hyperspectral imagery classification (HIC). By utilizing the tensor forms of hyperspectral pixels, we advance a tensor sparse-coding model which preserves as many original spatial constraints of a pixel and its spatial neighbors as possible. Furthermore, to alleviate the classification uncertainty resulted from widely existing mixed pixels, this paper constructs a regularization term for maximizing the likelihood of sparse-coding tensor defined on the posterior class probability. By combining the tensor sparse coding with maximizing likelihood estimation, a hybrid probabilistic SCC with spatial neighbor tensor (HPSCC-SNT) is proposed, which makes the pixels be well represented by the training pixels belonging to the same class. The performance of HPSCC-SNT is evaluated on three real hyperspectral imagery data sets, and the results show that it can achieve accurate and robust HIC results, and outperforms the state-of-the-art methods.
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content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2017.2732480