3D residual spatial–spectral convolution network for hyperspectral remote sensing image classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07933-8.pdf
Reference55 articles.
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2. Jia J, Wang Y, Chen J et al (2020) Status and application of advanced airborne hyperspectral imaging technology: a review. Infrared Phys Technol 104:103115. https://doi.org/10.1016/j.infrared.2019.103115
3. Sun H, Ren J, Zhao H et al (2019) Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images. Remote Sens. https://doi.org/10.3390/rs11050536
4. Firat H, Uçan M, Hanbay D (2021) Hyperspectral image classification using MiniVGGNet. J Comput Sci IDAP:295–303
5. Fırat H, Hanbay D (2021) 4CF-Net: hiperspektral uzaktan algılama görüntülerinin spektral uzamsal sınıflandırılması için yeni 3B evrişimli sinir ağı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Derg 1:439–453. https://doi.org/10.17341/gazimmfd.901291
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