Hyperspectral image classification using deep convolutional neural network and stochastic relaxation labeling
Author:
Affiliation:
1. MJP Rohilkhand University, Department of Computer Science and Information Technology, Bareilly, Utta
2. Michigan State University, Department of Biosystems and Agricultural Engineering, East Lansing, Mich
Publisher
SPIE-Intl Soc Optical Eng
Subject
General Earth and Planetary Sciences
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Hyperspectral image classification using improved multi-scale block local binary pattern and bi-exponential edge-preserving smoother;European Journal of Remote Sensing;2023-07-27
2. Fine Tuning the Pre-trained Convolutional Neural Network Models for Hyperspectral Image Classification Using Transfer Learning;Computer Vision and Robotics;2023
3. A Comparative Study of Different Convolution Neural Network Architectures for Hyperspectral Image Classification;2022 7th International Conference on Computing, Communication and Security (ICCCS);2022-11-03
4. Fusion of hyperspectral and LiDAR data using sparse stacked autoencoder for land cover classification with 3D-2D convolutional neural network;Journal of Applied Remote Sensing;2022-08-24
5. Hybrid Neural Network for Hyperspectral Satellite Image Classification (HNN);Intelligent Systems Design and Applications;2022
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