Awesome

Dunbin Shen¹ ,Xuanbing Zhu¹ ,Jiacheng Tian¹, Jianjun Liu², Zhenrong Du¹, Hongyu Wang¹, Xiaorui Ma^{1 :email:}

¹ Dalian University of Technology, ² Jiangnan University

(^:email:) Corresponding author.

ArXiv Preprint (arXiv 2407.06841)

News

• July 18th, 2024: We released our codes and models.️

Abstract

Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, HTD faces challenges due to limited prior knowledge and spectral variation, leading to underfitting models and unreliable performance. To address these challenges, this paper proposes an efficient self-supervised HTD method with a pyramid state space model (SSM), named HTD-Mamba, which employs spectrally contrastive learning to distinguish between target and background based on the similarity measurement of intrinsic features. Specifically, to obtain sufficient training samples and leverage spatial contextual information, we propose a spatial-encoded spectral augmentation technique that encodes all surrounding pixels within a patch into a transformed view of the center pixel. Additionally, to explore global band correlations, we divide pixels into continuous group-wise spectral embeddings and introduce Mamba to HTD for the first time to model long-range dependencies of the spectral sequence with linear complexity. Furthermore, to alleviate spectral variation and enhance robust representation, we propose a pyramid SSM as a backbone to capture and fuse multiresolution spectral-wise intrinsic features. Extensive experiments conducted on four public datasets demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative and qualitative evaluations.

Overview

Installation

• CUDA 11.7

  • Make sure /usr/local/cuda-11.7 exists. If not, you can install it from NVIDIA DEVELOPER (CUDA Toolkit). For example, Ubuntu 18.04 x86_64
    • wget https://developer.download.nvidia.com/compute/cuda/11.7.1/local_installers/cuda_11.7.1_515.65.01_linux.run
    • sudo sh cuda_11.7.1_515.65.01_linux.run
    Note that if you use Ubuntu 24.04, perhaps the gcc version is too high to install CUDA 11.7. So you should degrade the gcc version at first like this:
    • sudo apt install gcc-9 g++-9
    • sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 100
    • gcc -v
  • See nvcc -V is cuda_11.7. If not, you should modify the .bashrc like this:

    • vim ~/.bashrc -> i, and add the following to the end

      export CUDA_HOME=/usr/local/cuda-11.7

      export PATH=$PATH:/usr/bin:/bin

      export PATH=/usr/local/cuda-11.7/bin${PATH:+:${PATH}}

      export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

    • esc->:wq->source ~/.bashrc, and see nvcc -V is cuda_11.7.

• Python 3.10.x

  • conda create -n htd-mamba python=3.10
  • conda activate htd-mamba

• Torch 2.0.1

  • conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia or
  • pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu117

• Requirements: requirements.txt

  • cd /home/your_path/HTD-Mamba-main
  • pip install -r requirements.txt

• Install selective_scan_cuda

  • cd /home/your_path/HTD-Mamba-main
  • pip install .

• Install causal_conv1d

  • pip install --upgrade pip
  • pip install causal_conv1d>=1.1.0

Evaluation

To evaluate HTD-Mamba on four public datasets, run:

CL_Main_Sandiego.py  
CL_Main_Sandiego2.py
CL_Main_LosAngeles.py
CL_Main_Pavia.py

Training

• To train HTD-Mamba, change the state to train. After training, you can use the state select_best to select the best model from all epochs.
• To train HTD-Mamba on a new dataset, update the dataset, path, and band. For optimal performance, fine-tune parameters such as patch_size, m(group length), and channel(embedding size).

Acknowledgement

This project is based on Mamba (paper, code), Vim (paper, code). We thank the authors for their promising studies.

Citation

If you find HTD-Mamba useful in your research or applications, please consider giving us a star 🌟 and citing it using the following BibTeX entry.

 @article{shen2024htd,
  title={HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model},
  author={Shen, Dunbin and Zhu, Xuanbing and Tian, Jiacheng and Liu, Jianjun and Du, Zhenrong and Wang, Hongyu and Ma, Xiaorui},
  journal={arXiv preprint arXiv:2407.06841},
  year={2024}
}