A pixel-level fusion method for multi-source optical remote sensing image combining the principal component analysis and curvelet transform-Reference-Cited by-同舟云学术

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A pixel-level fusion method for multi-source optical remote sensing image combining the principal component analysis and curvelet transform

Published:2020-07-06 Issue:4 Volume:13 Page:1005-1013
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Chen Chao,He Xinyue,Guo Biyun,Zhao Xin,Chu Yanli

Funder

National Natural Science Foundation of China

Fundamental Research Funds for Zhejiang Provincial Universities and Research Institutes

Training Program of Excellent Master Thesis of Zhejiang Ocean University

State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

http://link.springer.com/content/pdf/10.1007/s12145-020-00472-7.pdf

Reference40 articles.

1. Ball JE, Anderson DT, Chan CS (2017) A comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. J Appl Remote Sens 11:042609

2. Bradley ES, Roberts DA, Dennison PE, Green RO, Eastwood M, Lundeen SR, McCubbin IB, Leifer I (2011) Google Earth and Google Fusion Tables in support of time-critical collaboration: Mapping the deepwater horizon oil spill with the AVIRIS airborne spectrometer. Earth Sci Inf 4:169–179

3. Chaib S, Liu H, Gu Y, Yao H (2017) Deep feature fusion for VHR remote sensing scene classification. IEEE Trans Geosci Remote Sens 55:4775–4784

4. Chen C, Qin Q, Zhang N, Li J, Chen L, Wang J, Qin X, Yang X (2014) Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology. Int J Remote Sens 35:3664–3682

5. Chen C, Fu J, Gai Y, Li J, Chen L, Mantravadi VS, Tan A (2018) Damaged bridges over water: using high-spatial-resolution remote-sensing images for recognition, detection, and assessment. IEEE Geosci Remote Sens Mag 6:69–85

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