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<p>HyperSpectral Image (HSI) processing has many applications in agriculture, Earth Change Monitoring, etc. Classification of HSIs is an important stage in most applications. Recently, transformers have shown outstanding performance in computer vision tasks and some transformer-based methods have been proposed for HSI classification. In this paper, we propose a novel classification algorithm using the convolutional mixer for HSI classification. Convolutional mixer is similar to transformers and is used to mix spatial and spectral information which are gathered separately by using depth wise convolution followed by a point wise convolution. We have used 3D convolutions in mixer block due to the 3D shape of hyperspectral cuboids. Experimental results show that our method is superior to most of state of the art works in terms of overall accuracy and Kappa Coefficient.</p>

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HyperSpectral Image Classification Using a 3D Convolutional Mixer Block

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • Sara Dianat,
  • Mehran Yazdi

摘要

HyperSpectral Image (HSI) processing has many applications in agriculture, Earth Change Monitoring, etc. Classification of HSIs is an important stage in most applications. Recently, transformers have shown outstanding performance in computer vision tasks and some transformer-based methods have been proposed for HSI classification. In this paper, we propose a novel classification algorithm using the convolutional mixer for HSI classification. Convolutional mixer is similar to transformers and is used to mix spatial and spectral information which are gathered separately by using depth wise convolution followed by a point wise convolution. We have used 3D convolutions in mixer block due to the 3D shape of hyperspectral cuboids. Experimental results show that our method is superior to most of state of the art works in terms of overall accuracy and Kappa Coefficient.