Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification
Hyperspectral data is a valuable source of both spectral and spatial information. However, to enhance the classification accuracy of hyperspectral image features, it is crucial to capture the spatial spectral features of image elements. The recent years have witnessed the potentials of deep learning...
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sg-ntu-dr.10356-1721952023-11-29T02:01:14Z Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification Wu, Guoqiang Ning, Xin Hou, Luyang He, Feng Zhang, Hengmin Shankar, Achyut School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Neural Network Hyperspectral Image Classification Hyperspectral data is a valuable source of both spectral and spatial information. However, to enhance the classification accuracy of hyperspectral image features, it is crucial to capture the spatial spectral features of image elements. The recent years have witnessed the potentials of deep learning methods have shown great promise in the hyperspectral image classification due to their ability to model complex structures and extract multiple features in an end-to-end fashion. Since hyperspectral images can be viewed as sequential data, we propose a novel three-dimensional Softmax mechanism-guided bidirectional GRU network (TDS-BiGRU) for HSI classification. By utilizing a bidirectional GRU to process the sequence data, our method can significantly reduce the processing time. Furthermore, the proposed three-dimensional Softmax mechanism leverages three branches to capture cross-latitude interactions and calculate Softmax weights, which enables us to obtain deeper features with greater discriminative power. The experimental results demonstrate that the proposed method outperforms several prevalent algorithms on four hyperspectral remote sensing datasets. Additionally, we conduct thorough comparisons and ablation tests, which further confirm the effectiveness of our approach. 2023-11-29T02:01:14Z 2023-11-29T02:01:14Z 2023 Journal Article Wu, G., Ning, X., Hou, L., He, F., Zhang, H. & Shankar, A. (2023). Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification. Signal Processing, 212, 109151-. https://dx.doi.org/10.1016/j.sigpro.2023.109151 0165-1684 https://hdl.handle.net/10356/172195 10.1016/j.sigpro.2023.109151 2-s2.0-85162154425 212 109151 en Signal Processing © 2023 Elsevier B.V. All rights reserved |
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Engineering::Electrical and electronic engineering Neural Network Hyperspectral Image Classification Wu, Guoqiang Ning, Xin Hou, Luyang He, Feng Zhang, Hengmin Shankar, Achyut Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification |
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Hyperspectral data is a valuable source of both spectral and spatial information. However, to enhance the classification accuracy of hyperspectral image features, it is crucial to capture the spatial spectral features of image elements. The recent years have witnessed the potentials of deep learning methods have shown great promise in the hyperspectral image classification due to their ability to model complex structures and extract multiple features in an end-to-end fashion. Since hyperspectral images can be viewed as sequential data, we propose a novel three-dimensional Softmax mechanism-guided bidirectional GRU network (TDS-BiGRU) for HSI classification. By utilizing a bidirectional GRU to process the sequence data, our method can significantly reduce the processing time. Furthermore, the proposed three-dimensional Softmax mechanism leverages three branches to capture cross-latitude interactions and calculate Softmax weights, which enables us to obtain deeper features with greater discriminative power. The experimental results demonstrate that the proposed method outperforms several prevalent algorithms on four hyperspectral remote sensing datasets. Additionally, we conduct thorough comparisons and ablation tests, which further confirm the effectiveness of our approach. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Wu, Guoqiang Ning, Xin Hou, Luyang He, Feng Zhang, Hengmin Shankar, Achyut |
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Article |
author |
Wu, Guoqiang Ning, Xin Hou, Luyang He, Feng Zhang, Hengmin Shankar, Achyut |
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Wu, Guoqiang |
title |
Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification |
title_short |
Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification |
title_full |
Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification |
title_fullStr |
Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification |
title_full_unstemmed |
Three-dimensional Softmax mechanism guided bidirectional GRU networks for hyperspectral remote sensing image classification |
title_sort |
three-dimensional softmax mechanism guided bidirectional gru networks for hyperspectral remote sensing image classification |
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2023 |
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https://hdl.handle.net/10356/172195 |
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1783955647981682688 |