Hybrid 3D/2D Complete Inception Module and Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software
Link
https://link.springer.com/content/pdf/10.1007/s11063-022-10929-z.pdf
Reference56 articles.
1. Chen C, Jiang F, Yang C et al (2018) Hyperspectral classification based on spectral–spatial convolutional neural networks. Eng Appl Artif Intell 68:165–171. https://doi.org/10.1016/j.engappai.2017.10.015
2. Roy SK, Chatterjee S, Bhattacharyya S et al (2020) Lightweight spectral-spatial squeeze-and- excitation residual bag-of-features learning for hyperspectral classification. IEEE Trans Geosci Remote Sens 58:5277–5290. https://doi.org/10.1109/TGRS.2019.2961681
3. 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
4. Firat H, Hanbay D (2021) 3B ESA Tabanlı ResNet50 Kullanılarak Hiperspektral Görüntülerin Sınıflandırılması Classification of Hyperspectral Images Using 3D CNN Based ResNet50. In: 2021 29th Signal Process Commun Appl Conf 6–9. https://doi.org/10.1109/SIU53274.2021.9477899
5. Ahmad M, Mazzara M, Distefano S (2021) Regularized cnn feature hierarchy for hyperspectral image classification. Remote Sens 13:1–11. https://doi.org/10.3390/rs13122275
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