DSFANet (Deep Slow Feature Analysis Network)

Implementation of Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images.

Abstract

In this paper, based on deep network and slow feature analysis (SFA) theory, we proposed a new change detection algorithm for multi-temporal remotes sensing images called Deep Slow Feature Analysis (DSFA). In DSFA model, two symmetric deep networks are utilized for projecting the input data of bi-temporal imagery. Then, the SFA module is deployed to suppress the unchanged components and highlight the changed components of the transformed features. The CVA pre-detection is employed to find unchanged pixels with high confidence as training samples. Finally, the change intensity is calculated with chi-square distance and the changes are determined by threshold algorithms. The experiments are performed on two real-world datasets and a public hyperspectral dataset. The visual comparison and quantitative evaluation have both shown that DSFA could outperform the other state-of-the-art algorithms, including other SFA-based and deep learning methods.

Requirements

tensorflow_gpu==1.7.0
scipy==1.0.0
numpy==1.14.0
matplotlib==2.1.2
tensorflow==1.14.0

Usage

Install the requirements

pip install -r requirements.txt

Run

python dsfa.py [-h] [-e EPOCH] [-l LR] [-r REG] [-t TRN] [-i ITER] [-g GPU]
               [--area AREA]

optional arguments:
  -h, --help              show this help message and exit
  -e EPOCH, --epoch EPOCH epoches
  -l LR, --lr LR          learning rate
  -r REG, --reg REG       regularization parameter
  -t TRN, --trn TRN       number of training samples
  -i ITER, --iter ITER    max iteration
  -g GPU, --gpu GPU       GPU ID
  --area AREA             datasets

Citation

Please cite our paper if you use this code in your research.

@article{du2018unsupervised,
  title={Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images},
  author={Du, Bo and Ru, Lixiang and Wu, Chen and Zhang, Liangpei},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2019}
}

The data in this repo is originally provided in GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection.

Q & A