Using deep neural networks for synthetic aperture radar image registration
Autor: | Dou Quan, Licheng Jiao, Tao Xiong, Mengdan Ning, Shuang Wang |
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Rok vydání: | 2016 |
Předmět: |
Synthetic aperture radar
020301 aerospace & aeronautics Computer science business.industry Deep learning Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Image registration Pattern recognition Speckle noise 02 engineering and technology RANSAC 0203 mechanical engineering Feature (computer vision) Robustness (computer science) Computer vision Artificial intelligence business 021101 geological & geomatics engineering |
Zdroj: | IGARSS |
Popis: | At present, the performance of image registration mainly depends on the extracted features in feature-based image registration. However, due to the speckle noise, synthetic aperture radar (SAR) image registration will have a lower accuracy and less robustness. For this purpose, we design a deep neural network (DNN) for SAR image registration, using the DNN to learn the image features, automatically. The deep learning could learn the more essential features of the images, which are make the image registration to achieve more robust features and accurate matching. Moreover, this paper proposed a new strategy to remove the wrong matching points based on the RANSAC. The experimental results on SAR image registration show that this image registration method based on DNN have a better performance, and the new RANSAC strategy could eliminate many wrong matching points and get a good transformational model. |
Databáze: | OpenAIRE |
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