Hyperspectral image classification based on local binary patterns and SVDNet

2019 ◽  
Author(s):  
Mengjie Sun ◽  
Feng Gao ◽  
Junyu Dong
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Local Binary Patterns ◽  
Hyperspectral Image Classification

Hyperspectral Image Classification via Discriminative Sparse Representation with Extended LBP Texture

2014 ◽  
Vol 989-994 ◽  
pp. 3885-3888 ◽  
Author(s):  
Yue Mei Ren ◽  
Yan Ning Zhang ◽  
Wei Wei
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Optimization Problem ◽  
Hyperspectral Image ◽  
Local Binary Patterns ◽  
Hyperspectral Image Classification ◽  
Texture Information ◽  
Classification Framework ◽  
Representation Error

Hyperspectral images (HSI) have rich texture information, so combining texture information and image spectral information can improve the recognition accuracy. Sparse representation has significant success in image classification. In this paper, we propose a new discriminative sparse-based classification framework using spectral data and extended Local Binary Patterns (LBP) texture. Firstly, we propose an extended LBP coding for HSI classification. Then we formulate an optimization problem that combines the objective function of classification with the representation error by sparsity. Furthermore, we use a procedure similar to K-SVD algorithm to learn the discriminative dictionary. The experimental results show that the proposed discriminative spasity-based classification of image including the extended LBP texture outperforms the classical HSI classification algorithms.

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