Deep concatenated features with improved heuristic-based recurrent neural network for hyperspectral image classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17351-0.pdf
Reference41 articles.
1. Zhu X, Li N, Pan Y (2019) Optimization Performance Comparison of Three Different Group Intelligence Algorithms on a SVM for Hyperspectral Imagery Classification. Remote Sens 11(6):734
2. Fang J (2020) Xiaoqian Cao “Multidimensional relation learning for hyperspectral image classification,.” Neurocomputing 410:211–219
3. Fang B, Li Y, Zhang H (2020) Jonathan Cheung-Wai Chan “Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples,.” ISPRS J Photogramm Remote Sens 161:164–178
4. Pan B, Shi Z (2018) Xia Xu “MugNet: Deep learning for hyperspectral image classification using limited samples,.” ISPRS J Photogramm Remote Sens 145:108–119
5. Ma Xiaorui, Wang Hongyu, Wang Jie (2016) Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning. ISPRS J Photogramm Remote Sens 120:99–107
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