MUS-CDB: Mixed Uncertainty Sampling With Class Distribution Balancing for Active Annotation in Aerial Object Detection-Reference-Cited by-同舟云学术

MUS-CDB: Mixed Uncertainty Sampling With Class Distribution Balancing for Active Annotation in Aerial Object Detection

Published:2023 Issue: Volume:61 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liang Dong¹^ORCID,Zhang Jing-Wei¹^ORCID,Tang Ying-Peng¹^ORCID,Huang Sheng-Jun¹^ORCID

Affiliation:

1. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Collaborative Innovation Center of Novel Software Technology and Industrialization, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10158738.pdf?arnumber=10158738

Reference55 articles.

1. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images

2. Using convolutional neural networks incorporating hierarchical active learning for target-searching in large-scale remote sensing images