# HSIMAE: A Unified Masked Autoencoder with large-scale pretraining for Hyperspectral Image Classification

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65979dfeb4b5c254cb8ed20e/vyn81w5ZnXWifmbX8By6T.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65979dfeb4b5c254cb8ed20e/ItHlquQYMlzo8wVMt_aVh.png)

## ✨ Highlights
### Masked HSI Modeling with Large-Scale Pretraining
The HSIMAE was pretrained by a large-scale HSI dataset, named HyspecNet-11k, then directly finetuned on four target classification datasets.

### Multi-Scale PCA for Features Extract
To address these distributional shifts caused by the different spectral resolutions and spectral ranges between hyperspectral sensors, a MS-PCA was used to extract the multi-scale features of HSI spectra and transform the raw spectra into fixed-length features.

### Dual-branch finetuning to leverage unlabeled data of target dataset
Dual-branch finetuning framework was proposed by using an extra unlabeled branch to further adapted the model to the distributions of the target dataset and suppressed the overfitting issue.

## 🧑‍💻 Contact

Wang Yue   
E-mail: ryanwy@csu.edu.cn
{"license": "apache-2.0", "tags": ["Hyperspectral image classification", "Mask autoencoder"]}
Hyperspectral image classification Mask autoencoder license:apache-2.0 region:us