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

By Bo Du, Lixiang Ru, Chen Wu, Liangpei Zhang. IEEE Transactions on Geoscience and Remote Sensing, 2018

308 citations recorded by the source corpus.

Change detection has been a hotspot in the remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detection algorithms have been proposed. Among these methods, image transformation methods with feature extraction and mapping could effectively highlight the changed information and thus has a better change detection performance. However, the changes of multi-temporal images are usually complex, and the existing methods are not effective enough. In recent years, the deep network has shown its brilliant performance in many fields, including feature extraction and projection. Therefore, in this paper, based on the deep network and slow feature analysis (SFA) theory, we proposed a new change detection algorithm for multi-temporal remotes sensing images called deep SFA (DSFA). In the 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 change vector analysis 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 data sets and a public hyperspectral data set. The visual comparison and the quantitative evaluation have shown that DSFA could outperform the other state-of-the-art algorithms, including other SFA-based and deep learning methods.

Subfields: Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/d5fe1d0101f0b4a880248c7574852fa05b3893fb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yuxing Chen
2. Hongruixuan Chen
3. Lorenzo Bruzzone
4. Le Wang
5. Chen Wu
6. Bo Du
7. Qingyang Li
8. Zhen Li

In Media Technology

1. Yuxing Chen
2. Bo Du
3. Le Wang
4. Chen Wu
5. Hongruixuan Chen
6. Lorenzo Bruzzone
7. Qingyang Li
8. Zhen Li

Citations in the corpus

• Self-Supervised Change Detection in Multiview Remote Sensing Images (2021, methodology): “[27] developed the slow feature analysis into deep learning methods to calculate the change intensity maps and highlight the changed components in the transformed feature space.”
• Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network (2020, methodology): “[29] for CD.”
• Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference (2021, background): “Based on the theory that changed and unchanged pixels correspond to quickly and slowly varying features in SFA, Du et al. [19] applied SFA in a convolutional network to suppress the spectral difference between slowly varying unchanged pixels and strengthen the changed pixels.”