Dimensionality reduction techniques with HydraNet framework for HSI classification

Alkhatib, Mohammed Q. and Al-Saad, Mina and Aburaed, Nour and Al Mansoori, Saeed and Al Ahmad, Hussain; (2022) Dimensionality reduction techniques with HydraNet framework for HSI classification. In: 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings. IEEE, FRA, pp. 3151-3155. ISBN 9781665496209 (https://doi.org/10.1109/ICIP46576.2022.9897740)

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Hyperspectral Imagery (HSI) classification is an important research area in remote sensing community due to its high efficiency in accurately analyzing ground features by assigning a class label to each pixel. This paper explores the use of Band Subset selection (BSS) methods as Dimensionality Reduction (DR) pre-processing stage for HSI classification, and compares them to Principal Component Analysis (PCA) approach. BSS is the problem of selecting the most independent bands in HSI cube. Classification is then performed using a proposed multi-branch HydraNet model that combines 1D, 2D, and 3D convolution. HydraNet is trained and tested using the benchmark Pavia University dataset, and the results are evaluated using Kappa and Overall Accuracy. Experimental results show positive indications of the network's performance, especially when compared to other state-of-the-art CNN networks.


Alkhatib, Mohammed Q., Al-Saad, Mina, Aburaed, Nour ORCID logoORCID: https://orcid.org/0000-0002-5906-0249, Al Mansoori, Saeed and Al Ahmad, Hussain;