Research on classification method of hyperspectral remote sensing images based on multiscale multichannel CNN

Ru Zhao; Chaozhu Zhang; Dan Xue

doi:10.1117/12.3028998

22 May 2024 Research on classification method of hyperspectral remote sensing images based on multiscale multichannel CNN

Ru Zhao, Chaozhu Zhang, Dan Xue

Author Affiliations +

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317609 (2024) https://doi.org/10.1117/12.3028998
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Convolutional neural network (CNN) is recognized and widely applied to classify hyperspectral remote sensing images (HSI). For the existing hyperspectral image classification algorithms, the network structure is simple, the generalization performance of the feature extraction model is poor, and the training samples are not uniform, this paper proposes a multi-scale multi-channel convolutional neural network (MMC-CNN) model based on the feature fusion. Firstly, the data is divided into two scales 𝑿_𝟏 × 𝑿_𝟏 pixel module and 𝑿_𝟐 × 𝑿_𝟐 pixel module, and then the two module channels are used again for feature extraction respectively, and three-dimensional convolution is used as the feature extractor in each channel. The experimental results show that the present model has a significant improvement in classification accuracy on commonly used HSI datasets, which is better than previous algorithms, thus verifying the high accuracy of the feature extraction of the present model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ru Zhao, Chaozhu Zhang, and Dan Xue "Research on classification method of hyperspectral remote sensing images based on multiscale multichannel CNN", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317609 (22 May 2024); https://doi.org/10.1117/12.3028998

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