Hyperspectral Image Classification using Spectral-Spatial Mixer Network
This work addresses the problem of accurate classification in remote sensing for researchers and practitioners, but it is incremental as it builds on existing spectral-spatial methods with minor architectural improvements.
The paper tackles hyperspectral image classification with limited labeled data by proposing SS-MixNet, a lightweight deep learning model that integrates 3D convolutions and mixer blocks, achieving 95.68% and 93.86% overall accuracy on two datasets using only 1% labeled data.
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