A hyperspectral image classification method based on 3D Conv-CapsNet

Wenming Liu; Mingyong Jiang

doi:10.1117/12.2650870

10 May 2023 A hyperspectral image classification method based on 3D Conv-CapsNet

Wenming Liu, Mingyong Jiang

Proceedings Volume 12554, AOPC 2022: Advanced Laser Technology and Applications; 1255405 (2023) https://doi.org/10.1117/12.2650870
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

To address the problems of insufficient utilization of spatial and spectral information and the need for large number of training samples in the current hyperspectral image classification methods based on deep learning, a algorithm(or model) named 3D Convolution Capsule Network (3D Conv-CapsNet) is investigated in this paper. A 3D convolution-capsule layer is constructed by combining the local connectivity in CNN and the capsule layer in Capsule Network. With the 3D convolution-capsule layer as the core, 3D Conv-CapsNet can acquire and utilize both spatial and spectral information of hyperspectral images, reduce the number of parameters and computational cost of the model, improve the feature representation capability of the capsule, and provide accurate classification results even in the case of limited training samples.In this paper, the classification results of 3D Conv-CapsNet on two hyperspectral image datasets, Salinas (SA) and Pavia University (PU), are analyzed and compared with SVM, 1D-CNN and 2D-CNN and 3D DenseNet. The experimental results show that, with 10% training samples, 3D Conv-CapsNet achieves 99.96%, 99.89% overall classification accuracy on SA and PU, which is better than SVM, 1D-CNN and 2D-CNN and 3D DenseNet. And the overall classification accuracy of 3D Conv-CapsNet network is still over 97% when the training samples are reduced to 5%, 3% and 1%.

Citation Download Citation

Wenming Liu and Mingyong Jiang "A hyperspectral image classification method based on 3D Conv-CapsNet", Proc. SPIE 12554, AOPC 2022: Advanced Laser Technology and Applications, 1255405 (10 May 2023); https://doi.org/10.1117/12.2650870

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KEYWORDS

3D modeling

3D image processing

Image classification

Hyperspectral imaging

Data modeling

3D acquisition

Statistical modeling

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