GSCCTL: a general semi-supervised scene classification method for remote sensing images based on clustering and transfer learning-Reference-Cited by-同舟云学术

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GSCCTL: a general semi-supervised scene classification method for remote sensing images based on clustering and transfer learning

Published:2022-01-05 Issue:15-16 Volume:43 Page:5976-6000
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Song Haifeng¹,Yang Weiwei¹

Affiliation:

1. School of Electronics and Information Engineering, Taizhou University, Taizhou (318000), Zhejiang, People’s Republic of China

Funder

the Cultivating Science Foundation of Taizhou University

the Science and Technology Project of Taizhou

the Zhejiang Provincial Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2021.2019851

Reference55 articles.

1. A New Technique for Remote Sensing Image Classification Based on Combinatorial Algorithm of SVM and KNN

2. Asymmetric Adaptation of Deep Features for Cross-Domain Classification in Remote Sensing Imagery

3. Deep Feature Fusion for VHR Remote Sensing Scene Classification

4. Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method

5. When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs

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