MixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors present MixerSENet, a new lightweight model for classifying hyperspectral images that keeps image size consistent and separates how it mixes spatial and color information. It includes a special squeeze and excitation block to improve how features are extracted, helping the model focus on important parts of the data. Tests on two standard datasets show MixerSENet performs better than existing advanced methods while using fewer parameters and running faster, making it practical for devices with limited computing power.
Hyperspectral Image ClassificationSpatial and Channel DimensionsSqueeze and Excitation BlockLightweight Neural Networks3D-CNNModel ParametersComputational EfficiencyOverall AccuracyBenchmark Datasets
Authors
Mohammed Q. Alkhatib, Swalpa Kumar Roy, Ali Jamali
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.