Liu, Chenying und Albrecht, Conrad M und Wang, Yi und Li, Qingyu und Zhu, Xiao Xiang (2024) AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation. IEEE Transactions on Geoscience and Remote Sensing, 62, Seite 5613917. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2024.3373908. ISSN 0196-2892.
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Offizielle URL: https://ieeexplore.ieee.org/document/10460569
Kurzfassung
While the volume of remote sensing data is increasing daily, deep learning in Earth Observation faces lack of accurate annotations for supervised optimization. Crowdsourcing projects such as OpenStreetMap distribute the annotation load to their community. However, such annotation inevitably generates noise due to insufficient control of the label quality, lack of annotators, frequent changes of the Earth's surface as a result of natural disasters and urban development, among many other factors. We present Adaptively trIggered Online Object-wise correction (AIO2) to address annotation noise induced by incomplete label sets. AIO2 features an Adaptive Correction Trigger (ACT) module that avoids label correction when the model training under- or overfits, and an Online Object-wise label Correction (O2C) methodology that employs spatial information for automated label modification. AIO2 utilizes a mean teacher model to enhance training robustness with noisy labels to both stabilize the training accuracy curve for fitting in ACT and provide pseudo labels for correction in O2C. Moreover, O2C is implemented online without the need to store updated labels every training epoch. We validate our approach on two building footprint segmentation datasets with different spatial resolutions. Experimental results with varying degrees of building label noise demonstrate the robustness of AIO2. Source code will be available at https://github.com/zhu-xlab/AIO2.git.
elib-URL des Eintrags: | https://elib.dlr.de/203149/ | ||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
Titel: | AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation | ||||||||||||||||||||||||
Autoren: |
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Datum: | 2024 | ||||||||||||||||||||||||
Erschienen in: | IEEE Transactions on Geoscience and Remote Sensing | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||
Band: | 62 | ||||||||||||||||||||||||
DOI: | 10.1109/TGRS.2024.3373908 | ||||||||||||||||||||||||
Seitenbereich: | Seite 5613917 | ||||||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||
ISSN: | 0196-2892 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | Building detection, curriculum learning, deep learning, early learning, label correction, memorization effects, noisy labels, remote sensing, semantic segmentation | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||
Hinterlegt von: | Liu, Chenying | ||||||||||||||||||||||||
Hinterlegt am: | 12 Apr 2024 14:47 | ||||||||||||||||||||||||
Letzte Änderung: | 12 Apr 2024 14:47 |
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