Style Transformation-Based Spatial-Spectral Feature Learning for Unsupervised Change Detection

Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the "same object with different spectra" problem is one of the biggest challeng...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 15
Main Authors: Liu, Ganchao, Yuan, Yuan, Zhang, Yuelin, Dong, Yongsheng, Li, Xuelong
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Attention mechanism
Automobiles
Change detection
Change detection algorithms
Detection
Detection algorithms
Feature extraction
Image reconstruction
Imaging
Imaging techniques
Innovation
Innovations
Machine learning
multispectral image (MSI)
Neural networks
Production facilities
Recurrent neural networks
Remote sensing
Spatial discrimination learning
Spectra
style transformation (ST)
Transparency (optical)
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the "same object with different spectra" problem is one of the biggest challenges in change detection. To overcome the challenge, a novel unsupervised spatial-spectral feature learning (FL) framework based on style transformation (ST) (called STFL-CD) is proposed for MSI change detection in this article. For dual-temporal MSIs, the proposed STFl-CD algorithm consists of two phases: ST and spatial-spectral FL. Since the image styles are inconsistent under different imaging environments, the first innovation is to transform the image styles through unmixing and reconstruction. Through ST, the challenge of the "same object with different spectra" problem will be reduced fundamentally. By introducing the attention mechanism, the other innovation is to extract the joint spectral-spatial change features based on a 3-D convolutional neural network with spatial and channel attention. In addition, for multitemporal MSIs, a multitemporal version STFL-CD (MT-STFL-CD) framework is designed based on a recurrent neural network to learn the correlation features between multitemporal remote sensing images. Both of the visual and quantitative results on the real MSI datasets indicate that the proposed unsupervised STFL-CD frameworks have significant advantages on multitemporal MSI change detection. In particular, the performance of the proposed unsupervised STFL-CD algorithm is even comparable to that of the state-of-the-art supervised or semisupervised methods.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3026099