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Remote Sensing Image Registration Based on Deep Learning Regression Model
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DOI：10.1109/LGRS.2020.3032439.png)
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摘要
En 中文
We propose a novel remote sensing image registration method based on the deep learning regression network. Different from the traditional methods of feature extraction and feature matching, we pair the image blocks from sensed and reference images, and then directly learn the displacement parameters of the four corners of the sensed image block relative to the reference image. In addition, we develop the dual deep learning network with weight sharing to fully extract the registration pair image features. The proposed method is tested on different period Landsat-7 and WorldView-3 images and compared with scale-invariant feature transform (SIFT), fast and rotated brief (ORB), and other deep learning methods. The proposed method outperforms all the comparing methods.
Keyword:
Feature extraction
Remote sensing
Image registration
Deep learning
Transforms
Training
Convolution
Convolutional neural network (CNN)
corner displacement
parametric regression
registration
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IF:
16.4
论文数:
1.0W
被引数:
5.1K
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