SELF-TRAINING FOR SEMI-SUPERVISED DEEP CONTOUR DETECTION OF SURFACE WATER
Abstract. Contour detection is better for monitoring dynamic and long-term changes to surface water bodies. For that purpose, we present a semi-automated method for collecting and labeling water contours from Landsat-8 and Sentinel-2 images. Due to the need for human inspection, the method has thus far generated 14K labeled images from more than 1.5M images. Given the cost of data labeling, we propose a deep semi-supervised self-learning system performed in two training stages, known as teacher-student. The teacher is trained on the accurate human-labeled data, then used to pseudo label the remaining unlabeled data. The student is trained on both human-labeled and machine pseudo-labeled data. For both teacher and student, we use a uniquely designed multiscale UNet classifier that uses fewer parameters and is more accurate than other state-of-the-art classifiers. Random augmentations are used to “noise” the student model and improve its generalization, and normalization schemes are used to blend the human-labeled loss with the machine-labeled loss. Comparisons to existing water body detection classifiers and segmentation classifiers show the superiority of our proposed system in detecting water contours.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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SELF-TRAINING FOR SEMI-SUPERVISED DEEP CONTOUR DETECTION OF SURFACE WATER ; volume:XLIII-B3-2022 ; year:2022 ; pages:1393-1398 ; extent:6
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLIII-B3-2022 (2022), 1393-1398 (gesamt 6)
- Urheber
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Alsamman, A.
Syed, M. B.
- DOI
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10.5194/isprs-archives-XLIII-B3-2022-1393-2022
- URN
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urn:nbn:de:101:1-2022060205240631978221
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:35 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Alsamman, A.
- Syed, M. B.
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