轻量级模型SS-MixNet提升高光谱图像分类精度
Hyperspectral Image Classification using Spectral-Spatial Mixer Network
- 融合3D卷积与并行MLP混频器，捕捉光谱空间长程依赖
- 仅用1%标注数据即达95.68%和93.86%准确率
- 适合小样本高光谱分类任务，计算开销低
本文提出SS-MixNet，一种轻量高效的高光谱图像（HSI）分类深度学习模型。该架构结合3D卷积层进行局部光谱-空间特征提取，并引入两个并行的MLP风格混频器块，分别捕获光谱与空间维度的长程依赖关系。采用基于深度可分离卷积的注意力机制，在极小计算开销下增强判别能力。模型在QUH-Tangdaowan与QUH-Qingyun数据集上仅使用1%标注数据进行训练与验证，性能超越2D-CNN、3D-CNN、IP-SWIN、SimPoolFormer及HybridKAN等方法，分别取得95.68%与93.86%的整体准确率。定量指标与分类图结果均证实其在有限监督下具备高精度与鲁棒性。代码将公开于：https://github.com/mqalkhatib/SS-MixNet。
原文摘要 · Abstract (English)
This paper introduces SS-MixNet, a lightweight and effective deep learning model for hyperspectral image (HSI) classification. The architecture integrates 3D convolutional layers for local spectral-spatial feature extraction with two parallel MLP-style mixer blocks that capture long-range dependencies in spectral and spatial dimensions. A depthwise convolution-based attention mechanism is employed to enhance discriminative capability with minimal computational overhead. The model is evaluated on the QUH-Tangdaowan and QUH-Qingyun datasets using only 1% of labeled data for training and validation. SS-MixNet achieves the highest performance among compared methods, including 2D-CNN, 3D-CNN, IP-SWIN, SimPoolFormer, and HybridKAN, reaching 95.68% and 93.86% overall accuracy on the Tangdaowan and Qingyun datasets, respectively. The results, supported by quantitative metrics and classification maps, confirm the model's effectiveness in delivering accurate and robust predictions with limited supervision. The code will be made publicly available at: https://github.com/mqalkhatib/SS-MixNet
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