Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910402407825408 |
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| author | Yang, Judy X Zhou, Jun Wang, Jing Tian, Hui Liew, Alan Wee Chung |
| author_facet | Yang, Judy X Zhou, Jun Wang, Jing Tian, Hui Liew, Alan Wee Chung |
| contents | Band selection in hyperspectral imaging (HSI) is critical for optimising data processing and enhancing analytical accuracy. Traditional approaches have predominantly concentrated on analysing spectral and pixel characteristics within individual bands independently. These approaches overlook the potential benefits of integrating multiple data sources, such as Light Detection and Ranging (LiDAR), and is further challenged by the limited availability of labeled data in HSI processing, which represents a significant obstacle. To address these challenges, this paper introduces a novel unsupervised band selection framework that incorporates attention mechanisms and an Autoencoder for reconstruction-based band selection. Our methodology distinctively integrates HSI with LiDAR data through an attention score, using a convolutional Autoencoder to process the combined feature mask. This fusion effectively captures essential spatial and spectral features and reduces redundancy in hyperspectral datasets. A comprehensive comparative analysis of our innovative fused band selection approach is performed against existing unsupervised band selection and fusion models. We used data sets such as Houston 2013, Trento, and MUUFLE for our experiments. The results demonstrate that our method achieves superior classification accuracy and significantly outperforms existing models. This enhancement in HSI band selection, facilitated by the incorporation of LiDAR features, underscores the considerable advantages of integrating features from different sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder Yang, Judy X Zhou, Jun Wang, Jing Tian, Hui Liew, Alan Wee Chung Computer Vision and Pattern Recognition F.2.2, I.2.7 Band selection in hyperspectral imaging (HSI) is critical for optimising data processing and enhancing analytical accuracy. Traditional approaches have predominantly concentrated on analysing spectral and pixel characteristics within individual bands independently. These approaches overlook the potential benefits of integrating multiple data sources, such as Light Detection and Ranging (LiDAR), and is further challenged by the limited availability of labeled data in HSI processing, which represents a significant obstacle. To address these challenges, this paper introduces a novel unsupervised band selection framework that incorporates attention mechanisms and an Autoencoder for reconstruction-based band selection. Our methodology distinctively integrates HSI with LiDAR data through an attention score, using a convolutional Autoencoder to process the combined feature mask. This fusion effectively captures essential spatial and spectral features and reduces redundancy in hyperspectral datasets. A comprehensive comparative analysis of our innovative fused band selection approach is performed against existing unsupervised band selection and fusion models. We used data sets such as Houston 2013, Trento, and MUUFLE for our experiments. The results demonstrate that our method achieves superior classification accuracy and significantly outperforms existing models. This enhancement in HSI band selection, facilitated by the incorporation of LiDAR features, underscores the considerable advantages of integrating features from different sources. |
| title | Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder |
| topic | Computer Vision and Pattern Recognition F.2.2, I.2.7 |
| url | https://arxiv.org/abs/2404.05258 |