HSIMAE：A Unified Masked Autoencoder with Large-scale Pretraining for Hyperspectral Image Classification

✨ 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.

A group-wise PCA was used to extract features of HSI spectra and transform the raw spectra to fixed-length features.

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.

🔨 Installation

1. Install Anaconda or Miniconda

2. Install Git

3. Open commond line, create environment and enter with the following commands:

   conda create -n HSIMAE python=3.8
   conda activate HSIMAE

4. Clone the repository and enter:

   git clone https://github.com/Ryan21wy/HSIMAE.git
   cd HSIMAE

5. Install dependency with the following commands:

   pip install -r requirements.txt

🚀 Checkpoint

The pre-training dataset and pretrained models of HSIMAE are provided in Hugging Face.

Because it is too big, HySpecNet-11k need be downloaded from HySpecNet-11k - A Large-Scale Hyperspectral Benchmark Dataset (rsim.berlin)

🧐 Evaluation Results

Classification Dataset:

Salinas: Salinas scene

Pavia University: Pavia University

Houston 2013: 2013 IEEE GRSS Data Fusion Contest

WHU-Hi-LongKou: WHU-Hi: UAV-borne hyperspectral and high spatial resolution (H2) benchmark datasets

Results

Overall accuracy of four HSI classification datasets. The training set and validation set contained 5/10/15/20 random samples per class , respectively, and the remaining samples were considered as the test set.

✏️ Citation

If you think this project is helpful, please feel free to leave a star⭐️ and cite our paper:

@ARTICLE{10607879,
  author={Wang, Yue and Wen, Ming and Zhang, Hailiang and Sun, Jinyu and Yang, Qiong and Zhang, Zhimin and Lu, Hongmei},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 
  title={HSIMAE: A Unified Masked Autoencoder with Large-scale Pre-training for Hyperspectral Image Classification}, 
  year={2024},
  doi={10.1109/JSTARS.2024.3432743}
}

🧑‍💻 Contact

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