# Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks

arXiv: https://arxiv.org/abs/2202.06458 · HTML: https://mlsanity.com/paper/2202.06458/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)

## Built on by (most cited first, 11 of 11)

- [DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification](https://mlsanity.com/paper/2307.06667/) (2023)
- [Spatial-Spectral Hyperspectral Classification based on Learnable 3D Group Convolution](https://mlsanity.com/paper/2307.07720/) (2023)
- [Efficient Dynamic Attention 3D Convolution for Hyperspectral Image Classification](https://mlsanity.com/paper/2503.23472/) (2025)
- [Spatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.04463/) (2025)
- [3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.10795/) (2025)
- [Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.13045/) (2025)
- [Classification of grapevine varieties using UAV hyperspectral imaging](https://mlsanity.com/paper/2401.12851/) (2024)
- [Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.15155/) (2025)
- [Hyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive Gating](https://mlsanity.com/paper/2506.08324/) (2025)
- [Spatial Bias for Attention-free Non-local Neural Networks](https://mlsanity.com/paper/2302.12505/) (2023)
- [MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture](https://mlsanity.com/paper/2507.04409/) (2025)

## Abstract (arXiv)

Hyperspectral imagery is rich in spatial and spectral information. Using 3D-CNN can simultaneously acquire features of spatial and spectral dimensions to facilitate classification of features, but hyperspectral image information spectral dimensional information redundancy. The use of continuous 3D-CNN will result in a high amount of parameters, and the computational power requirements of the device are high, and the training takes too long. This letter designed the Faster selective kernel mechanism network (FSKNet), FSKNet can balance this problem. It designs 3D-CNN and 2D-CNN conversion modules, using 3D-CNN to complete feature extraction while reducing the dimensionality of spatial and spectrum. However, such a model is not lightweight enough. In the converted 2D-CNN, a selective kernel mechanism is proposed, which allows each neuron to adjust the receptive field size based on the two-way input information scale. Under the Selective kernel mechanism, it mainly includes two components, se module and variable convolution. Se acquires channel dimensional attention and variable convolution to obtain spatial dimension deformation information of ground objects. The model is more accurate, faster, and less computationally intensive. FSKNet achieves high accuracy on the IN, UP, Salinas, and Botswana data sets with very small parameters.