Splitting detectors: semisupervised remote sensing object detection

Yuanjun Chen; Yu Liu; Zhizhuo Jiang; You He

doi:10.1117/12.3032649

16 August 2024 Splitting detectors: semisupervised remote sensing object detection

Yuanjun Chen, Yu Liu, Zhizhuo Jiang, You He

Proceedings Volume 13218, First Aerospace Frontiers Conference (AFC 2024); 132181U (2024) https://doi.org/10.1117/12.3032649
Event: First Aerospace Frontiers Conference (AFC 2024), 2024, Xi’an, China

Abstract

Our work revolves around remote sensing object detection, where the scarcity of annotation information poses a significant challenge and hinders adequate training. To address this issue, we concentrate on semi-supervised object detection methods (SSOD), which offer a promising solution into alleviate the problem of limited labeling information.However, in our specific scenario, the self-training nature of these methods, coupled with the filtering mechanism of pseudo-labeled frames, tends to amplify noise due to the arbitrary angular rotation of remote sensing data objects. In light of these challenges, we propose a novel remote sensing task-specific SSOD framework called Splitting Detectors (SLD), which mitigates noise accumulation caused by remote sensing data characteristics. SLD has the following two innovations: (1) The SLD decouples detectors, mitigating task conflicts and thus reducing noise accumulation. (2) The SLD uses the mean-teacher semi-supervised method to train the classification head and the self-supervised method for the regression head respectively, which reduce inaccurate pseudo coordinates error caused by rotation. The Experiments on dota-split datasets demonstrate the considerable superiority of our proposed framework to other state-of-the-arts.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuanjun Chen, Yu Liu, Zhizhuo Jiang, and You He "Splitting detectors: semisupervised remote sensing object detection", Proc. SPIE 13218, First Aerospace Frontiers Conference (AFC 2024), 132181U (16 August 2024); https://doi.org/10.1117/12.3032649

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KEYWORDS

Object detection

Education and training

Remote sensing

Head

Sensors

Data modeling

Image classification

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