Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification
Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hy...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/146021 |
| _version_ | 1811685726658494464 |
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| author | Yang, Xiaofei Zhang, Xiaofeng Ye, Yunming Lau, Raymond Y. K. Lu, Shijian Li, Xutao Huang, Xiaohui |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Yang, Xiaofei Zhang, Xiaofeng Ye, Yunming Lau, Raymond Y. K. Lu, Shijian Li, Xutao Huang, Xiaohui |
| author_sort | Yang, Xiaofei |
| collection | NTU |
| description | Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D/3D CNNs. |
| first_indexed | 2024-10-01T04:49:06Z |
| format | Journal Article |
| id | ntu-10356/146021 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:49:06Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1460212021-01-21T06:05:49Z Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification Yang, Xiaofei Zhang, Xiaofeng Ye, Yunming Lau, Raymond Y. K. Lu, Shijian Li, Xutao Huang, Xiaohui School of Computer Science and Engineering Engineering::Computer science and engineering Convolutional Neural Network 3D CNN Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D/3D CNNs. Published version 2021-01-21T06:05:49Z 2021-01-21T06:05:49Z 2020 Journal Article Yang, X., Zhang, X., Ye, Y., Lau, R. Y. K., Lu, S., Li, X., & Huang, X. (2020). Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification. Remote Sensing, 12(12), 2033-. doi:10.3390/rs12122033 2072-4292 0000-0002-5751-4550 https://hdl.handle.net/10356/146021 10.3390/rs12122033 2-s2.0-85086986483 12 12 en Remote Sensing © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering::Computer science and engineering Convolutional Neural Network 3D CNN Yang, Xiaofei Zhang, Xiaofeng Ye, Yunming Lau, Raymond Y. K. Lu, Shijian Li, Xutao Huang, Xiaohui Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification |
| title | Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification |
| title_full | Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification |
| title_fullStr | Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification |
| title_full_unstemmed | Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification |
| title_short | Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification |
| title_sort | synergistic 2d 3d convolutional neural network for hyperspectral image classification |
| topic | Engineering::Computer science and engineering Convolutional Neural Network 3D CNN |
| url | https://hdl.handle.net/10356/146021 |
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