Slow Feature Analysis for Change Detection in Multispectral Imagery

By Chen Wu, Bo Du, Liangpei Zhang. IEEE Transactions on Geoscience and Remote Sensing, 2014

329 citations recorded by the source corpus.

Subfields: Atmospheric Science, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/549fcde12d071b55f2e0f2a494de491e7fb97140

How researchers see this paper

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

In Atmospheric Science

1. Bo Du
2. Lichao Mou
3. Lorenzo Bruzzone
4. Liangpei Zhang
5. Le Wang
6. Yuxing Chen
7. Hongruixuan Chen
8. Chen Wu

In Biomedical Engineering

1. Chen Wu
2. Bo Du
3. Lorenzo Bruzzone
4. Lichao Mou
5. Yuxing Chen
6. Hui Lu
7. Haobo Lyu
8. Liangpei Zhang

In Media Technology

1. Bo Du
2. Lorenzo Bruzzone
3. Liangpei Zhang
4. Lichao Mou
5. Pedram Ghamisi
6. Le Wang
7. Xiaoxiang Zhu
8. Yuxing Chen

Citations in the corpus

• Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference (2021, methodology): “…(CVA) [12], [13], principal component analysis (PCA) [14], [15], independent component analysis (ICA) [16]), image classification [17], [18], and other advanced algorithms, such as slow feature analysis (SFA) [18]–[20] and iteratively reweighted multivariate alteration detection (IR-MAD) [21].”
• Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images (2018, methodology): “Inspired by this idea, slow feature analysis is proposed for detecting real changes and obtained satisfactory performance [28], [29].”
• ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model (2024, background): “Due to the increased heterogeneity within the same land-cover feature brought about by the increase in spatial resolution, the traditional pixel-based CD methods [7]–[9] are difficult to achieve satisfactory detection results.”
• SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution (2023, background): “3224987 classification [4], and change detection [5], [6], [7].”
• Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network (2020, methodology): “Wu et al [16] proposed a novel CD method based on slow feature analysis (SFA), which aims to find the most invariant component in multitemporal images to highlight changed regions.”
• Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection (2016, methodology): “Wu et al. [19] had labeled some test samples according to a detailed visual analysis of multi-temporal images and some prior information, and these labeled samples are also used to quantitatively evaluate the performance of REFEREE and its competitors.”
• Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “MSI measures the radiation inherent to an object, regardless of an external light source [52–55].”
• Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery (2018, background): “SFA [13] is able to extract the most temporally invariant component from multitemporal images to transform data into a new feature space and, in this space, differences in unchanged pixels are suppressed so that changed regions can be better separated.”
• DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images (2020, background): “analysis [51] can extract invariant features from multitemporal remote sensing image data, reduce the radiation differences of unchanged pixels, and improve the separability between”
• Self-Supervised Change Detection in Multiview Remote Sensing Images (2021, methodology): “[22] proposed a novel change detection method to project the bi-temporal images into a”
• Deep Recurrent Neural Networks for Hyperspectral Image Classification (2017, background): “To benefit from this type of data, supervised hyperspectral image classification plays a significant role and has been investigated in many applications, including urban development [2]–[5], the monitoring of land changes [6]–[9], scene interpretation [10]–[13], and resource management [14], [15].”