High Resolution Remote Sensing Image Classification Based on Deep Transfer Learning and Multi Feature Network
To improve the automatic classification accuracy of remote sensing images, this study raises a high-resolution remote sensing image classification model that combines deep transfer learning and multi-feature network. In this paper, deep transfer learning is the core technology of remote sensing imag...
Main Author: | Xinyan Huang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10267994/ |
Similar Items
-
Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions
by: Wei Hu, et al.
Published: (2021-11-01) -
Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification
by: Biserka Petrovska, et al.
Published: (2020-07-01) -
A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification
by: Cuiping Shi, et al.
Published: (2021-05-01) -
DRSNet: Novel architecture for small patch and low-resolution remote sensing image scene classification
by: Feihao Chen, et al.
Published: (2021-12-01) -
HDTFF-Net: Hierarchical Deep Texture Features Fusion Network for High-Resolution Remote Sensing Scene Classification
by: Wanying Song, et al.
Published: (2023-01-01)