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Hyperspectral image classification using improved multi-scale block local binary pattern and bi-exponential edge-preserving smoother

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成果类型:
期刊论文
作者:
Wan, Xiaoqing;Chen, Shuanghao
通讯作者:
Wan, XQ
作者机构:
[Wan, Xiaoqing] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
[Chen, Shuanghao] Zhengzhou Univ, Coll Comp & Engn, Zhengzhou, Peoples R China.
通讯机构:
[Wan, XQ ] H
Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
语种:
英文
关键词:
Classification;hyperspectral image;multiple strategy fusion;multi-scale block local binary pattern;edge-preserving filtering
期刊:
European Journal of Remote Sensing
ISSN:
1129-8596
年:
2023
卷:
56
期:
1
基金类别:
This research was supported by the Hengyang Normal University Fund Project (2022QD07).
机构署名:
本校为第一且通讯机构
院系归属:
计算机科学与技术学院
摘要:
In this paper, a multi-strategy fusion (MSF) framework, based on improved MBLBP and bi-exponential edge-preserving smoother (BEEPS), is proposed for hyperspectral image (HSI) classification. First, MBLBP operator is adopted to characterize the overall structural information of HSI, where the averaging strategy allocates same weights for the pixels in a local sub-region, so that the edges tend to be blurred due to being isotropic. To solve this question, the steering kernel is first introduced into MBLBP for learning the local structure prior of...

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