Remote Sensing Image Registration Using Convolutional Neural Network Features
Successful remote sensing image registration is an important step for many remote sensing applications. The scale-invariant feature transform (SIFT) is a well-known method for remote sensing image registration, with many variants of SIFT proposed. However, it only uses local low-level information, a...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2018-02, Vol.15 (2), p.232-236 |
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| container_title | IEEE geoscience and remote sensing letters |
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| creator | Ye, Famao Su, Yanfei Xiao, Hui Zhao, Xuqing Min, Weidong |
| description | Successful remote sensing image registration is an important step for many remote sensing applications. The scale-invariant feature transform (SIFT) is a well-known method for remote sensing image registration, with many variants of SIFT proposed. However, it only uses local low-level information, and loses much middle- or high-level information to register. Image features extracted by a convolutional neural network (CNN) have achieved the state-of-the-art performance for image classification and retrieval problems, and can provide much middle- and high-level information for remote sensing image registration. Hence, in this letter, we investigate how to calculate the CNN feature, and study the way to fuse SIFT and CNN features for remote sensing image registration. The experimental results demonstrate that the proposed method yields a better registration performance in terms of both the aligning accuracy and the number of correct correspondences. |
| doi_str_mv | 10.1109/LGRS.2017.2781741 |
| format | Article |
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The scale-invariant feature transform (SIFT) is a well-known method for remote sensing image registration, with many variants of SIFT proposed. However, it only uses local low-level information, and loses much middle- or high-level information to register. Image features extracted by a convolutional neural network (CNN) have achieved the state-of-the-art performance for image classification and retrieval problems, and can provide much middle- and high-level information for remote sensing image registration. Hence, in this letter, we investigate how to calculate the CNN feature, and study the way to fuse SIFT and CNN features for remote sensing image registration. 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The scale-invariant feature transform (SIFT) is a well-known method for remote sensing image registration, with many variants of SIFT proposed. However, it only uses local low-level information, and loses much middle- or high-level information to register. Image features extracted by a convolutional neural network (CNN) have achieved the state-of-the-art performance for image classification and retrieval problems, and can provide much middle- and high-level information for remote sensing image registration. Hence, in this letter, we investigate how to calculate the CNN feature, and study the way to fuse SIFT and CNN features for remote sensing image registration. 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| subjects | Artificial neural networks Convolutional neural network (CNN) Detection Feature extraction Image classification Image registration Levels Methods Neural networks Registers Registration Remote sensing remote sensing image registration Robustness scale-invariant feature transform (SIFT) State of the art Transforms |
| title | Remote Sensing Image Registration Using Convolutional Neural Network Features |
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