A transfer learning framework based on adaptive spatial spectral relational networks for hyperspectral image classification

Sen Yuan; Mengbin Rao; Jianjun Ge

doi:10.1117/12.2675408

25 May 2023 A transfer learning framework based on adaptive spatial spectral relational networks for hyperspectral image classification

Sen Yuan, Mengbin Rao, Jianjun Ge

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Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263608 (2023) https://doi.org/10.1117/12.2675408
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Hyperspectral image classification is one of the key technologies in hyperspectral image application. Since the number of bands in hyperspectral images is usually much larger than that in RGB images or multispectral images, it is necessary to reduce the dimensionality of the hyperspectral data of the target by using pre-trained models from other fields. However, this situation may lead to the loss of effective spectral information. Therefore, to overcome this problem, we propose a novel transfer learning framework based on Adaptive Spatial Spectral Relational Networks (ASSRN), which can be applied to knowledge transfer between different hyperspectral datasets. Finally, we conduct extensive experiments on several databases. The inspiring experimental results demonstrate the effectiveness of the proposed framework.

Citation Download Citation

Sen Yuan, Mengbin Rao, and Jianjun Ge "A transfer learning framework based on adaptive spatial spectral relational networks for hyperspectral image classification", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263608 (25 May 2023); https://doi.org/10.1117/12.2675408

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KEYWORDS

Hyperspectral imaging

Image classification

Spatial learning

Data modeling

Feature extraction

Sensors

3D modeling

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