Hyperspectral Image Classification using Spectral-Spatial Mixer Network

TL;DR

This paper presents SS-MixNet, a lightweight deep learning model that effectively classifies hyperspectral images using spectral-spatial features and attention mechanisms, achieving state-of-the-art accuracy with minimal labeled data.

Contribution

The paper introduces SS-MixNet, a novel spectral-spatial mixer network combining 3D convolutions and attention for hyperspectral image classification with limited supervision.

Findings

Achieves over 95% accuracy on Tangdaowan dataset

Outperforms existing methods like 2D-CNN, 3D-CNN, and HybridKAN

Effective with only 1% labeled training data

Abstract

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