# A 3D 2D convolutional Neural Network Model for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2111.10293 · HTML: https://mlsanity.com/paper/2111.10293/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.04621/) (2019)

## Abstract (arXiv)

In the proposed SEHybridSN model, a dense block was used to reuse shallow feature and aimed at better exploiting hierarchical spatial spectral feature. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial spectral features was realized by the channel attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer. Experiment results indicate that our proposed model learn more discriminative spatial spectral features using very few training data. SEHybridSN using only 0.05 and 0.01 labeled data for training, a very satisfactory performance is obtained.