Robust Matching for SAR and Optical Images Using Multiscale Convolutional Gradient Features

Image matching is a key preprocessing step for the integrated application of synthetic aperture radar (SAR) and optical images. Due to significant nonlinear intensity differences between such images, automatic matching for them is still quite challenging. Recently, structure features have been effec...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Zhou, Liang, Ye, Yuanxin, Tang, Tengfeng, Nan, Ke, Qin, Yao
Format: Article
Language:English
Subjects:
Adaptive optics
Artificial neural networks
Deep learning
Feature extraction
Feature maps
Image matching
Kernel
Methods
multiscale convolutional gradient feature (MCGF)
Night vision
Optical imaging
Optical sensors
pseudo-Siamese network
Radar imaging
SAR (radar)
Synthetic aperture radar
synthetic aperture radar (SAR) and optical images
Template matching
Training
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Summary:Image matching is a key preprocessing step for the integrated application of synthetic aperture radar (SAR) and optical images. Due to significant nonlinear intensity differences between such images, automatic matching for them is still quite challenging. Recently, structure features have been effectively applied to SAR-to-optical image matching because of their robustness to nonlinear intensity differences. However, structure features designed by handcraft are limited to achieve further improvement. Accordingly, this letter employs the deep learning technique to refine structure features for improving image matching. First, we extract multiorientated gradient features to depict the structure properties of images. Then, a shallow pseudo-Siamese network is built to convolve the gradient feature maps in a multiscale manner, which produces the multiscale convolutional gradient features (MCGFs). Finally, MCGF is used to achieve image matching by a fast template scheme. MCGF can capture finer common features between SAR and optical images than traditional handcrafted structure features. Moreover, it also can overcome some limitations of current matching methods based on deep learning, which requires solving a huge number of model parameters by a large number of training samples. Two sets of SAR and optical images with different resolutions are used to evaluate the matching performance of MCGF. The experimental results show its advantage over other state-of-the-art methods.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3105567