MixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification

2026-06-01

AI Summary

This work addresses the challenges of low computational efficiency and scarce labeled data in hyperspectral image classification by proposing a lightweight MixerSENet framework. The method decouples spatial and channel-wise mixing operations and integrates fixed-size image patch inputs with Squeeze-and-Excitation attention mechanisms to enhance feature representation while substantially reducing model complexity. With only 53,146 parameters, the proposed model achieves overall accuracies of 82.47% and 96.70% on the Houston13 and Qingyun datasets, respectively, demonstrating both high inference efficiency and superior performance compared to several state-of-the-art approaches.

Abstract

In this paper, a novel framework, MixerSENet, is introduced for hyperspectral image (HSI) classification, designed to address the challenges of computational efficiency and limited labeled data. The proposed model processes hyperspectral image patches while maintaining consistent size and resolution throughout the network, effectively decoupling the mixing of spatial and channel dimensions. Notably, MixerSENet is lightweight and computationally efficient, requiring fewer parameters compared to traditional models, making it suitable for resource-constrained environments. A squeeze and excitation block is incorporated into the model to refine feature extraction, enhancing the network's ability to capture more informative features. Experimental results on two benchmark datasets demonstrate that MixerSENet achieves superior performance, reaching an overall accuracy (OA) of 82.47% on Houston13 dataset and 96.70% on the Qingyun dataset, outperforming state-of-the-art methods including 3D-CNN, HybridKAN, HSIFormer, SimPoolFormer, and MorphMamba. Furthermore, a detailed analysis of computational efficiency shows that MixerSENet achieves a favorable balance between accuracy and efficiency, with only 53,146 parameters and an low inference time, confirming its practicality for real-world applications. At publication, source code will be publicly available at https://github.com/mqalkhatib/MixerSENet.

Problem

• hyperspectral image classification
• computational efficiency
• limited labeled data
• lightweight model
• resource-constrained environments

Innovation

• MixerSENet
• lightweight architecture
• spatial-channel decoupling
• squeeze-and-excitation
• hyperspectral image classification