Spatial and class structure regularized sparse representation graph for semi-supervised hyperspectral image classification-Reference-Cited by-同舟云学术

Spatial and class structure regularized sparse representation graph for semi-supervised hyperspectral image classification

Published:2018-09 Issue: Volume:81 Page:81-94
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Shao Yuanjie,Sang Nong,Gao Changxin^ORCID,Ma Li

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference53 articles.

1. Learning group-based sparse and low-rank representation for hyperspectral image classification;He;Pattern Recognit.,2016

2. Hyperspectral image reconstruction by deep convolutional neural network for classification;Li;Pattern Recognit.,2017

3. Regularized set-to-set distance metric learning for hyperspectral image classification;Peng;Pattern Recognit. Lett.,2016

4. Joint bilateral filtering and spectral similarity-based sparse representation: a generic framework for effective feature extraction and data classification in hyperspectral imaging;Qiao;Pattern Recognit.,2017

5. Superpixel-based 3d deep neural networks for hyperspectral image classification;Shi;Pattern Recognit.,2018

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