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Hyperspectral image classification by integrating attention-based LSTM and hybrid spectral networks


Citation

Ahmed AL-Kubaisi, Mohammed and Shafri, Helmi Zulhaidi Mohd and Ismail, Mohd Hasmadi and Yusof, Mohd Johari Mohd and Hashim, Shaiful Jahari (2022) Hyperspectral image classification by integrating attention-based LSTM and hybrid spectral networks. International Journal of Remote Sensing, 43 (1). 3450 - 3469. ISSN 0143-1161; ESSN: 1366-5901

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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Additional Metadata

Item Type: Article
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
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