Classification of Rethinking Hyperspectral Images using 2D and 3D CNN with Channel and Spatial Attention: A Review
by Muhammad Ahsan Aslam 1,* 
, Muhammad Tariq Ali 2, Sunwan Nawaz 1, Saima Shahzadi 3, Muhammad Ali Fazal 2
1 Institute of Computer Science, Khwaja Fareed University of Engineering & Information Technology, RYK, 64200, Pakistan
2 IT Department, Khwaja Fareed University of Engineering & Information Technology, RYK, 64200, Pakistan
3 Computer Science Department, University of Agriculture, Faisalabad, 38000, Pakistan
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 2, Issue 4, Page # 22-32, 2023; DOI: 10.55708/js0204003
Keywords: Hyperspectral, Image classification, Deep learning, Convolutional neural network, Feature extraction, Spectral-spatial features, Machine Learning
Received: 09 September 2022, Revised: 03 February 2023, Accepted: 31 March 2023, Published Online: 28 April 2023
APA Style
Aslam, M. A., Ali, M. T., Nawaz, S., Shahzadi, S., & Fazal, M. A. (2023). Classification of Rethinking Hyperspectral Images using 2D and 3D CNN with Channel and Spatial Attention: A Review. Journal of Engineering Research and Sciences, 2(4), 22–32. https://doi.org/10.55708/js0204003
Chicago/Turabian Style
Aslam, Muhammad Ahsan, Muhammad Tariq Ali, Sunwan Nawaz, Saima Shahzadi, and Muhammad Ali Fazal. “Classification of Rethinking Hyperspectral Images using 2D and 3D CNN with Channel and Spatial Attention: A Review.” Journal of Engineering Research and Sciences 2, no. 4 (February 1, 2023): 22–32. https://doi.org/10.55708/js0204003.
IEEE Style
M. A. Aslam, M. T. Ali, S. Nawaz, S. Shahzadi, and M. A. Fazal, “Classification of Rethinking Hyperspectral Images using 2D and 3D CNN with Channel and Spatial Attention: A Review,” Journal of Engineering Research and Sciences, vol. 2, no. 4, pp. 22–32, Feb. 2023, doi: 10.55708/js0204003.
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Abstract
It has been demonstrated that 3D Convolutional Neural Networks (CNN) are an effective technique for classifying hyperspectral images (HSI). Conventional 3D CNNs produce too many parameters to extract the spectral-spatial properties of HSIs. A channel service module and a spatial service module are utilized to optimize characteristic maps and enhance sorting performance in order to further study discriminating characteristics. In this article, evaluate CNN’s methods for hyperspectral image categorization (HSI). Examined the replacement of traditional 3D CNN with mixed feature maps by frequency to lessen spatial redundancy and expand the receptive field. Evaluates several CNN stories that use image classification algorithms, elaborating on the efficacy of these approaches or any remaining holes in methods. How do improve those gaps for better image classification?
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