Classification of Hyperspectral Images Using Conventional Neural Networks
Author:
Publisher
Allerton Press
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation
Link
https://link.springer.com/content/pdf/10.3103/S8756699021020102.pdf
Reference10 articles.
1. M. Borhani and H. Ghassemian, ‘‘Hyperspectral image classification based on non-uniform spatialspectral kernels,’’ in Proc. of the Iranian Conf. on Intelligent Systems, Bam, Iran, 2014 https://doi.org/10.1109/IranianCIS.2014.6802579.
2. S. M. Borzov and O. I. Potaturkin, ‘‘Efficiency of the spectral-spatial classification of hyperspectral imaging data,’’ Optoelectron., Instrum. Data Process. 53, 26–34 (2017). https://doi.org/10.3103/S8756699017010058
3. S. M. Borzov and O. I. Potaturkin, ‘‘Classification of hyperspectral images with different methods of training set formation,’’ Optoelectron., Instrum. Data Process. 54, 76–82 (2018). https://doi.org/10.3103/S8756699018010120
4. S. M. Borzov and O. I. Potaturkin, ‘‘Spectral-spatial methods for hyperspectral image classification. Review,’’ Optoelectron., Instrum. Data Process. 54, 582–599 (2018). https://doi.org/10.3103/S8756699018060079
5. B. Fang, Y. Li, H. Zhang, and J. Ch.-W. Chan, ‘‘Hyperspectral images classification based on dense convolutional networks with spectral-wise attention mechanism,’’ Remote Sens. 11, 159 (2019). https://doi.org/10.3390/rs11020159
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