MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2604.26138v1
• Date: Tue, 28 Apr 2026 21:53:16 GMT
• Title: MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification
• Abstract summary: This paper presents MixerCA, a novel lightweight model for hyperspectral image (HSI) classification.<n>It integrates depth-wise convolutions, token and channel mixing, and coordinate attention into a unified structure to decouple spatial and channel interactions.<n>Experiments on four hyperspectral benchmark datasets reveal MixerCA's clear advantages over several competing algorithms.
• Score: 6.875312133832078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past decade, hyperspectral image (HSI) classification has drawn considerable interest due to HSIs' ability to effectively distinguish terrestrial objects by capturing detailed, continuous spectral information. The strong performance of recent deep learning techniques in tasks like image classification and semantic segmentation has led to their growing use in HSI classification, due to their ability to capture complex spatial and spectral features more effectively than traditional methods. This paper presents MixerCA, a novel lightweight model for HSI classification that leverages depthwise convolution and a self-attention mechanism. MixerCA integrates depth-wise convolutions, token and channel mixing, and coordinate attention into a unified structure to decouple spatial and channel interactions, maintain consistent resolution throughout the network, and directly process HSI patches. Extensive experiments on four hyperspectral benchmark datasets reveal MixerCA's clear advantages over several competing algorithms, including 2D-CNN, 3D-CNN, Tri-CNN, HybridSN, ViT, and Swin Transformer. The source code is publicly available at https://github.com/mqalkhatib/MixerCA.

Related papers

• A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification [10.687430702802608]
  In hyperspectral image (HSI) classification, each pixel is categorized into a specific land-cover category or material.<n>Recent works have utilized a multi-scale vision transformer (ViT) to enhance spectral feature capture and yield promising results.<n>We propose a synergistic CNN-Transformer network with pooling attention fusion for HSI classification.
  arXiv  Detail & Related papers  (2026-04-26T09:30:27Z)
• Hyperspectral Image Classification using Spectral-Spatial Mixer Network [2.538209532048867]
  This paper introduces SS-MixNet, a lightweight and effective deep learning model for hyperspectral image (HSI) classification.<n>The architecture integrates 3D convolutional layers for local spectral-spatial feature extraction with two parallel-style mixer blocks.<n>The model is evaluated on the QUH-Tangdaowan and QUH-Qingyun datasets using only 1% of labeled data.
  arXiv  Detail & Related papers  (2025-11-19T18:48:52Z)
• 3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification [12.729885732069926]
  Vision Transformers (ViTs) have shown promising classification performance over Convolutional Neural Networks (CNNs) ViTs excel with sequential data, but they cannot extract spectral-spatial information like CNNs. We propose a 3D-Convolution guided Spectral-Spatial Transformer (3D-ConvSST) for HSI classification.
  arXiv  Detail & Related papers  (2024-04-20T03:39:54Z)
• Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising [54.110544509099526]
  Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. We propose a hybrid convolution and attention network (HCANet) to enhance HSI denoising. Experimental results on mainstream HSI datasets demonstrate the rationality and effectiveness of the proposed HCANet.
  arXiv  Detail & Related papers  (2024-03-15T07:18:43Z)
• Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images [73.01141916544103]
  Hyperspectral images (HSI) clustering is an important but challenging task.<n>We first use 3-D and 2-D hybrid convolutional neural networks to extract the high-order spatial and spectral features of HSI.<n>We then design a superpixel graph contrastive clustering model to learn discriminative superpixel representations.
  arXiv  Detail & Related papers  (2024-03-04T07:40:55Z)
• DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification [109.09061514799413]
  Hyperspectral image (HSI) classification is challenging due to spatial variability caused by complex imaging conditions. We propose a tri-spectral image generation pipeline that transforms HSI into high-quality tri-spectral images. Our proposed method outperforms state-of-the-art methods for HSI classification.
  arXiv  Detail & Related papers  (2023-04-19T18:32:52Z)
• Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction [138.04956118993934]
  We propose a novel Transformer-based method, coarse-to-fine sparse Transformer (CST) CST embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing.
  arXiv  Detail & Related papers  (2022-03-09T16:17:47Z)
• Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution [80.93870349019332]
  We propose a novel HSISR method that uses Transformer instead of CNN to learn the prior of HSIs. Specifically, we first use the gradient algorithm to solve the HSISR model, and then use an unfolding network to simulate the iterative solution processes.
  arXiv  Detail & Related papers  (2021-11-27T15:38:57Z)
• Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network [9.583523548244683]
  Deep convolutional neural networks (CNNs) have shown impressive ability to represent hyperspectral images (HSIs) We propose a novel end-to-end, pixels-to-pixels fully convolutional spatial propagation network (FCSPN) for HSI classification. FCSPN consists of a 3D fully convolution network (3D-FCN) and a convolutional spatial propagation network (CSPN)
  arXiv  Detail & Related papers  (2020-08-04T09:05:52Z)
• Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution [79.97180849505294]
  We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI. Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
  arXiv  Detail & Related papers  (2020-07-10T08:08:20Z)
• Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning [36.05574127972413]
  We first deliver a 3D asymmetric inception network, AINet, to overcome the overfitting problem. With the emphasis on spectral signatures over spatial contexts of HSI data, AINet can convey and classify the features effectively.
  arXiv  Detail & Related papers  (2020-02-11T06:37:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.