---
license: apache-2.0
tags:
- Hyperspectral image classification
- Mask autoencoder
---
# HSIMAE: A Unified Masked Autoencoder with large-scale pretraining for Hyperspectral Image Classification

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65979dfeb4b5c254cb8ed20e/YjbxlXg5el3nySkcQkmq_.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65979dfeb4b5c254cb8ed20e/dxLbojSBr4Kdt-cgA3su7.png)

## ✨ Highlights
### Large-Scale and Diverse Dataset for HSI Pretraining
A large and diverse HSI dataset named HSIHybrid was curated for large-scale HSI pre-training. It consisted of 15 HSI datasets from different hyperspectral sensors. After splitting into image patches, a total of **4 million** HSI patches with a spatial size of 9×9 were obtained.

### New MAE Architecture for HSI domain
A modified MAE named HSIMAE that utilized separate spatial-spectral encoders followed by fusion blocks to learn spatial correlation and spectral correlation of HSI data was proposed.

### Dual-branch finetuning to leverage unlabeled data of target dataset
A dual-branch fine-tuning framework was introduced to leverage the unlabeled data of the downstream HSI dataset and suppressed overfitting on small training samples.

## 🧑‍💻 Contact

Wang Yue   
E-mail: ryanwy@csu.edu.cn