Rotation-Invariant Feature Learning for Object Detection in VHR Optical Remote Sensing Images by Double-Net

Rotation-Invariant Feature Learning for Object Detection in VHR Optical Remote Sensing Images by Double-Net

Rotation-invariant feature extraction is crucial for object detection in Very High Resolution (VHR) optical remote sensing images. Although convolutional neural networks (CNNs) are good at extracting the translation-invariant features and have been widely applied in computer vision, it is still a ch...

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Bibliographic Details
Main Authors: Zhi Zhang, Ruoqiao Jiang, Shaohui Mei, Shun Zhang, Yifan Zhang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Convolution neural network (CNN)
feature learning
object detection
rotation-invariant
very high resolution (VHR)
Online Access:https://ieeexplore.ieee.org/document/8936929/

Internet

https://ieeexplore.ieee.org/document/8936929/

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