A 3D-convolutional-autoencoder embedded Siamese-attention-network for classification of hyperspectral images

The classification of hyperspectral images (HSI) into categories that correlate to various land cover sorts such as water bodies, agriculture and urban areas, has gained significant attention in research due to its wide range of applications in fields, such as remote sensing, computer vision, and mo...

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Veröffentlicht in:	Neural computing & applications Jg. 36; H. 15; S. 8335 - 8354
Hauptverfasser:	Ranjan, Pallavi, Kumar, Rajeev, Girdhar, Ashish
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.05.2024 Springer Nature B.V
Schlagworte:	Artificial Intelligence Artificial neural networks Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer vision Data Mining and Knowledge Discovery Hyperspectral imaging Image classification Image Processing and Computer Vision Land cover Machine learning Original Article Probability and Statistics in Computer Science Remote sensing Deep learning Siamese Hyperspectral images Autoencoder Classification Attention
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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