Support vector machine regression (SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization (RRN)-Reference-Cited by-同舟云学术

Support vector machine regression (SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization (RRN)

Published:2020-07-02 Issue:3 Volume:23 Page:237-247
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Geng Jing¹²^ORCID,Gan Wenxia³^ORCID,Xu Jinying³,Yang Ruqin⁴,Wang Shuliang¹²^ORCID

Affiliation:

1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

2. Beijing Institute of Technology, Beijing, China

3. School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, China

4. Wuhan Natural Resources and Planning Information Center, Wuhan, China

Funder

National Natural Science Fund of China

Science fund project of Wuhan Institute of Technology

Science and Technology Development Funds Project of Department of Transportation of Hubei Province

Publisher

Informa UK Limited

Subject

Computers in Earth Sciences,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2020.1785958

Reference31 articles.

1. Multitemporal Remote Sensing

2. Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery

3. Cross-Validation Methods

4. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation

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