Tnesorflow implementation for Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images.
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.
tensorflow_gpu==1.7.0
scipy==1.0.0
numpy==1.14.0
matplotlib==2.1.2
- Note: This dataset was firstly provided in GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection.
pip install -r requirements.txt
| Predicted Results | Reference Image |
|---|---|
@article{Du2019Unsupervised,
author={B. {Du} and L. {Ru} and C. {Wu} and L. {Zhang}},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images},
year={2019},
volume={57},
number={12},
pages={9976-9992},
doi={10.1109/TGRS.2019.2930682},
ISSN={1558-0644},
month={Dec},
}