Deep clustering using 3D attention convolutional autoencoder for hyperspectral image analysis

Deep clustering has been widely applicated in various fields, including natural image and language processing. However, when it is applied to hyperspectral image (HSI) processing, it encounters challenges due to high dimensionality of HSI and complex spatial-spectral characteristics. This study intr...

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Veröffentlicht in:Scientific reports Jg. 14; H. 1; S. 4209 - 13
Hauptverfasser: Zheng, Ziyou, Zhang, Shuzhen, Song, Hailong, Yan, Qi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 20.02.2024
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/117
639/705/258
Algorithms
Classification
Clustering
Data reduction
Deep learning
Embedding
Humanities and Social Sciences
Image processing
multidisciplinary
Neural networks
Principal components analysis
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Deep clustering has been widely applicated in various fields, including natural image and language processing. However, when it is applied to hyperspectral image (HSI) processing, it encounters challenges due to high dimensionality of HSI and complex spatial-spectral characteristics. This study introduces a kind of deep clustering model specifically tailed for HSI analysis. To address the high dimensionality issue, redundant dimension of HSI is firstly eliminated by combining principal component analysis (PCA) with t- distributed stochastic neighbor embedding (t-SNE). The reduced dataset is then input into a three-dimensional attention convolutional autoencoder (3D-ACAE) to extract essential spatial-spectral features. The 3D-ACAE uses spatial-spectral attention mechanism to enhance captured features. Finally, these enhanced features pass through an embedding layer to create a compact data-representation, and the compact data-representation is divided into distinct clusters by clustering layer. Experimental results on three publicly available datasets validate the superiority of the proposed model for HSI analysis.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-54547-2