MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification
Over the past decade, hyperspectral image (HSI) classification has drawn considerable interest due to HSIs' ability to effectively distinguish terrestrial objects by capturing detailed, continuous spectral information. The strong performance of recent deep learning techniques in tasks like image classification and semantic segmentation has led to their growing use in HSI classification, due to their ability to capture complex spatial and spectral features more effectively than traditional methods. This paper presents MixerCA, a novel lightweight model for HSI classification that leverages depthwise convolution and a self-attention mechanism. MixerCA integrates depth-wise convolutions, tok...
| Authors | Mohammed Q. Alkhatib, Ali Jamali |
|---|---|
| Primary category | cs.CV |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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