Fusion High-and-Low-Level Features via Ridgelet and Convolutional Neural Networks for Very High-Resolution Remote Sensing Imagery Classification

Fusion High-and-Low-Level Features via Ridgelet and Convolutional Neural Networks for Very High-Resolution Remote Sensing Imagery Classification

Ridgelet can theoretically approximate low-level image features, and the ridgelet filter is constructed independently of the training sample, but it usually requires to preset a lot of parameters to achieve an ideal representation of complex scenes. Convolutional neural networks (CNNs) can adaptivel...

Full description

Bibliographic Details
Main Authors: Zhifeng Zheng, Jiannong Cao
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
VHR remote sensing images
classification
multi-resolution neural networks
ridgelet filter
convolutional features
Online Access:https://ieeexplore.ieee.org/document/8805303/

Internet

https://ieeexplore.ieee.org/document/8805303/

Similar Items