Citation
Abstract
Though hyperspectral remote sensing images contain rich spatial and spectral information, they pose challenges in terms of feature extraction and mining. This paper describes the integration of a dimensionality reduction technique that employs spectral attention and Hybrid Spectral Networks (HybridSN) with spatial attention for hyperspectral image classification. The goal of this approach is to improve the ability to classify hyperspectral images by increasing the capabilities of spectral-spatial feature fusion. Experiments on three hyperspectral datasets (Indian Pines, University of Pavia, and Houston University) demonstrate that our method’s overall accuracy is 99.66%, 99.97%, and 99.17% under 20% of the training samples, respectively, which is superior to several well-known approaches.
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Official URL or Download Paper: https://www.tandfonline.com/doi/abs/10.1080/014311...
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Design and Architecture Faculty of Engineering Faculty of Forestry |
DOI Number: | https://doi.org/10.1080/01431161.2022.2093621 |
Publisher: | Taylor & Francis |
Keywords: | Convolutional neural network; Hyperspectral image classification; LSTM autoencoder; Spectral-spatial attention |
Depositing User: | Ms. Che Wa Zakaria |
Date Deposited: | 05 Aug 2024 07:32 |
Last Modified: | 05 Aug 2024 07:32 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/01431161.2022.2093621 |
URI: | http://psasir.upm.edu.my/id/eprint/101758 |
Statistic Details: | View Download Statistic |
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