Multitask Fine-Grained Feature Mining for Multilabel Remote Sensing Image Classification-Reference-Cited by-同舟云学术

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Multitask Fine-Grained Feature Mining for Multilabel Remote Sensing Image Classification

Published:2024 Issue: Volume:62 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Guo Jie¹^ORCID,Sun Hao¹^ORCID,Han Jinheng²,Song Bin¹^ORCID,Chi Yuhao¹^ORCID,Song Bingxi³

Affiliation:

1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China

2. Hangzhou Institute of Technology, Xidian University, Hangzhou, China

3. Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control, Shijiazhuang, China

Funder

Key Research and Development Program of Shaanxi

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Proof of Concept Foundation of Xidian University Hangzhou Institute of Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10595130.pdf?arnumber=10595130

Reference61 articles.

1. Multi-Label Remote Sensing Scene Classification Using Multi-Bag Integration

2. Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification

3. Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

4. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods

5. Semantic Classification of Urban Trees Using Very High Resolution Satellite Imagery

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