Semi-supervised object detection with uncurated unlabeled data for remote sensing images

Semi-supervised object detection with uncurated unlabeled data for remote sensing images

Annotating remote sensing images (RSIs) poses a significant challenge, primarily due to its labor-intensive nature. Semi-supervised object detection (SSOD) methods address this challenge by generating pseudo-labels for unlabeled data, assuming that all classes present in the unlabeled dataset are al...

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Bibliographic Details
Main Authors: Nanqing Liu, Xun Xu, Yingjie Gao, Yitao Zhao, Heng-Chao Li
Format: Article
Language:English
Published: Elsevier 2024-05-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Remote sensing images
Semi-supervised learning
Open-set semi-supervised learning
Object detection
Memory bank
Adaptive threshold
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843224001687

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http://www.sciencedirect.com/science/article/pii/S1569843224001687

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