MixerCA用轻量结构提升高光谱图像分类精度与效率。
MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification
- 结合深度卷积与自注意力，解耦空间与通道交互
- 在4个基准数据集上超越2D/3D-CNN、ViT等模型
- 适合追求高精度且算力受限的高光谱分类任务
过去十年，高光谱图像（HSI）分类因能通过连续光谱信息有效区分地表物体而受到广泛关注。深度学习在图像分类与语义分割中的优异表现推动其在HSI分类中的应用，因其能更有效地捕捉复杂的空间与光谱特征。本文提出MixerCA，一种新颖的轻量级HSI分类模型，融合深度卷积与自注意力机制。MixerCA将深度卷积、令牌与通道混合、坐标注意力整合到统一结构中，实现空间与通道交互的解耦，保持网络全程一致分辨率，并直接处理HSI块。在四个高光谱基准数据集上的大量实验表明，MixerCA显著优于多种竞争方法，包括2D-CNN、3D-CNN、Tri-CNN、HybridSN、ViT和Swin Transformer。源代码已公开于https://github.com/mqalkhatib/MixerCA。
原文摘要 · Abstract (English)
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, token and channel mixing, and coordinate attention into a unified structure to decouple spatial and channel interactions, maintain consistent resolution throughout the network, and directly process HSI patches. Extensive experiments on four hyperspectral benchmark datasets reveal MixerCA's clear advantages over several competing algorithms, including 2D-CNN, 3D-CNN, Tri-CNN, HybridSN, ViT, and Swin Transformer. The source code is publicly available at https://github.com/mqalkhatib/MixerCA.
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