A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

2017 ◽  
Author(s):  
Wei Li ◽  
Lan Chang ◽  
Mengmeng Zhang
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Coarse To Fine

Hyperspectral Image Classification via Multiple-Feature-Based Improved Sparse Representation

2019 ◽  
Vol 39 (5) ◽  
pp. 0528004
Author(s):  
李非燕 Li Feiyan ◽  
霍宏涛 Huo Hongtao ◽  
李静 Li Jing ◽  
白杰 Bai Jie
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Multiple Feature ◽  
Feature Based

Locality-constrained sparse representation for hyperspectral image classification

2021 ◽  
Vol 546 ◽  
pp. 858-870
Author(s):  
Yuanshu Zhang ◽  
Yong Ma ◽  
Xiaobing Dai ◽  
Hao Li ◽  
Xiaoguang Mei ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification

scholarly journals A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification

2020 ◽  
Vol 12 (4) ◽  
pp. 664 ◽  
Author(s):  
Binge Cui ◽  
Jiandi Cui ◽  
Yan Lu ◽  
Nannan Guo ◽  
Maoguo Gong
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Information Entropy ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Training Set ◽  
Training Samples ◽  
Labeling Method

Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult. In this paper, we propose a novel sample pseudo-labeling method based on sparse representation (SRSPL) for hyperspectral image classification, in which sparse representation is used to select the purest samples to extend the training set. The proposed method consists of the following three steps. First, intrinsic image decomposition is used to obtain the reflectance components of hyperspectral images. Second, hyperspectral pixels are sparsely represented using an overcomplete dictionary composed of all training samples. Finally, information entropy is defined for the vectorized sparse representation, and then the pixels with low information entropy are selected as pseudo-labeled samples to augment the training set. The quality of the generated pseudo-labeled samples is evaluated based on classification accuracy, i.e., overall accuracy, average accuracy, and Kappa coefficient. Experimental results on four real hyperspectral data sets demonstrate excellent classification performance using the new added pseudo-labeled samples, which indicates that the generated samples are of high confidence.

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