Snapshot ensemble-based residual network (SnapEnsemResNet) for remote sensing image scene classification-Reference-Cited by-同舟云学术

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Snapshot ensemble-based residual network (SnapEnsemResNet) for remote sensing image scene classification

Published:2023-03-06 Issue:2 Volume:27 Page:341-372
ISSN:1384-6175
Container-title:GeoInformatica
language:en
Short-container-title:Geoinformatica

Author:

Siddiqui Muhammad Ibraheem,Khan Khurram,Fazil Adnan,Zakwan Muhammad

Publisher

Springer Science and Business Media LLC

Subject

Geography, Planning and Development,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s10707-023-00492-7.pdf

Reference62 articles.

1. Hu Q et al (2013) Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping. Remote Sensing 5(11):6026–6042

2. Gómez-Chova L, Tuia D, Moser G, Camps-Valls G (2015) Multimodal classification of remote sensing images: A review and future directions. Proc IEEE 103(9):1560–1584

3. Longbotham N, Chaapel C, Bleiler L, Padwick C, Emery WJ, Pacifici F (2011) Very high resolution multiangle urban classification analysis. IEEE Trans Geosci Remote Sens 50(4):1155–1170

4. Huang X, Wen D, Li J, Qin R (2017) Multi-level monitoring of subtle urban changes for the megacities of China using high-resolution multi-view satellite imagery. Remote Sens Environ 196:56–75

5. Zhang T, Huang X (2018) Monitoring of urban impervious surfaces using time series of high-resolution remote sensing images in rapidly urbanized areas: A case study of Shenzhen. IEEE J Sel Top Appl Earth Observations Remote Sensing 11(8):2692–2708

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