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Hyperspectral image classification via parallel multi-input mechanism-based convolutional neural network

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Abstract

In recent years, Convolutional Neural Networks (CNNs) have succeeded in Hyperspectral Image Classification and shown excellent performance. However, the implicit spatial information between features, which significantly affect the classification performance of CNNs, are neglected in most existing CNN models. To address this issue, we propose a parallel multi-input mechanism-based CNN (PMI-CNN) fully exploiting the implicit spectral-spatial information in Hyperspectral Images. PMI-CNN employs four parallel convolution branches to extract spatial features with different levels, feature maps from each branch are spliced, and used as the classifier’s input. The proposed PMI-CNN’s classification performance is examined on three benchmark datasets and compared with six competing models. Experimental results show that PMI-CNN has better classification performance via exploiting spectral-spatial information. Compared with other models, the classification accuracy of PMI-CNN on the Indian Pines dataset is significantly improved, varying between 1.23%-25.36%. Likewise, the PMI-CNN, performed on the other two benchmark datasets, achieves 0.54%-12.26% and 0.96%-8.38% advantages in overall accuracy over the other six models, respectively.

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Funding

This paper was supported by the Open Fund of Hubei Key Laboratory of Intelligent Geo-Information Processing (Grant No. ZRIGIP-201801).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, No.68 Jincheng Road, Hongshan District, Wuhan, China

    Huan Zhong, Li Li, Jiansi Ren, Wei Wu & Ruoxiang Wang

  2. Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, No.68 Jincheng Road, Hongshan District, Wuhan, China

    Jiansi Ren

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  1. Huan Zhong
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  2. Li Li
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Contributions

All the authors made significant contributions to the work. Huan Zhong, Li Li designed the research, analysed the results, accomplished the validation work, and finished the final paper writing. Jiansi Ren, Wei Wu, and Ruoxiang Wang provided advice for the preparation and revision of the paper.

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Correspondence to Jiansi Ren.

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The authors declare that they have no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results.

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Zhong, H., Li, L., Ren, J. et al. Hyperspectral image classification via parallel multi-input mechanism-based convolutional neural network. Multimed Tools Appl 81, 24601–24626 (2022). https://doi.org/10.1007/s11042-022-12494-y

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