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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Vydané v:Neural computing & applications Ročník 36; číslo 15; s. 8335 - 8354
Hlavní autori: Ranjan, Pallavi, Kumar, Rajeev, Girdhar, Ashish
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.05.2024
Springer Nature B.V
Predmet:
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
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Shrnutí: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 more. Supervised deep learning networks have demonstrated exceptional performance in HSI classification, capitalizing on their capacity for end-to-end optimization and leveraging their strong potential for nonlinear modeling. However, labelling HSIs, on the other hand, necessitates extensive domain knowledge and is a time-consuming and labour-intensive exercise. To address this issue, the proposed work introduces a novel semi-supervised network constructed with an autoencoder, Siamese action, and attention layers that achieves excellent classification accuracy with labelled limited samples. The proposed convolutional autoencoder is trained using the mass amount of unlabelled data to learn the refinement representation referred to as 3D-CAE. The added Siamese network improves the feature separability between different categories and attention layers improve classification by focusing on discriminative information and neglecting the unimportant bands. The efficacy of the proposed model’s performance was assessed by training and testing on both same-domain as well as cross-domain data and found to achieve 91.3 and 93.6 for Indian Pines and Salinas, respectively.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-09527-y