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DOI: 10.14569/IJACSA.2022.0131202
PDF

Hyperspectral Image Segmentation using End-to-End CNN Architecture with built-in Feature Compressor for UAV Systems

Author 1: Muhammad Bilal
Author 2: Khalid Munawar
Author 3: Muhammad Shafique Shaikh
Author 4: Ubaid M. Al-Saggaf
Author 5: Belkacem Kada

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

  • Abstract and Keywords
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Abstract: Hyperspectral image segmentation is an important task for geographical surveying. Real-time processing of this operation is especially important for sensors mounted on-board Unmanned Aerial Vehicles in the context of visual servoing, landmarks recognition and data compression for efficient storage and transmission. To this end, this paper proposes a machine learning approach for segmentation using an efficient Convolutional Neural Network (CNN) which incorporates a feature compressor and a subsequent segmentation module based on 3D convolution operations. The experimental results demonstrate that the proposed approach gives segmentation accuracy at par with conventional approaches based on Principal Component Analysis (PCA) to reduce the feature dimensionality. Moreover, the proposed network is at least 35% faster than the conventional CNN-based approaches using 3D convolutions.

Keywords: Hyperspectral images; CNN; dimensionality reduction; segmentation; PCA

Muhammad Bilal, Khalid Munawar, Muhammad Shafique Shaikh, Ubaid M. Al-Saggaf and Belkacem Kada, “Hyperspectral Image Segmentation using End-to-End CNN Architecture with built-in Feature Compressor for UAV Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131202

@article{Bilal2022,
title = {Hyperspectral Image Segmentation using End-to-End CNN Architecture with built-in Feature Compressor for UAV Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131202},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131202},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Muhammad Bilal and Khalid Munawar and Muhammad Shafique Shaikh and Ubaid M. Al-Saggaf and Belkacem Kada}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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