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Relaxation linear regression with spectral–spatial constrained locality adaptive regularization for hyperspectral image classification

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成果类型:
期刊论文
作者:
Yang, Meng-Long;Long, Chen-Feng;Deng, Yang-Jun;Luo, Xiang
通讯作者:
Long, CF
作者机构:
[Long, Chen-Feng; Deng, Yang-Jun; Yang, Meng-Long] Hunan Agr Univ, Coll Informat & Intelligence, Changsha, Peoples R China.
[Luo, Xiang] China Guangdong Tabacco Meizhou Ltd, Informat Dept, Meizhou, Peoples R China.
通讯机构:
[Long, CF ] H
Hunan Agr Univ, Coll Informat & Intelligence, Changsha, Peoples R China.
语种:
英文
关键词:
hyperspectral imaging;image classification;regression analysis
期刊:
Electronics Letters
ISSN:
0013-5194
年:
2024
卷:
60
期:
3
页码:
e13108-
基金类别:
Natural Science Foundation of Hunan Province; Key Research and Development Program of Hunan Province of China [2023NK2011]; Meizhou Tobacco Science Research Project [202204]; [2022JJ40189]
机构署名:
本校为第一且通讯机构
摘要:
This letter proposes a novel relaxation linear regression with spectral–spatial constrained locality adaptive regularization (SSLA‐RLR) method for HSI classification. The SSLA‐RLR method not only integrates the locality adaptive graph with relaxation linear regression to adaptively exploit the local geometrical structure for relieving the side effects of noise corruptions, but also takes the spatial correlations between each data point and its neighbours into consideration via spectral–spatial constraint. Abstract Recently, relaxation linea...

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