DEEP LEARNING-BASED STEREO MATCHING FOR HIGH-RESOLUTION SATELLITE IMAGES: A COMPARATIVE EVALUATION
Abstract. Dense matching plays an important role in 3D modeling from satellite images. Its purpose is to establish pixel-by-pixel correspondences between two stereo images. The most well-known algorithm is the semi-global matching (SGM), which can generate high-quality 3D models with high computational efficiency. Due to the complex coverage and imaging condition, SGM cannot cope with these situation well. In recent years, deep learning-based stereo matching has attracted wide attention and shown overwhelming benefits over traditional algorithms in terms of precision and completeness. However, existing models are usually evaluated by using close-ranging datasets. Thus, this study investigates the recent deep learning models and evaluate their performance on both close-ranging and satellite image datasets. The results demonstrate that deep learning network can better adapt to the satellite dataset than the typical SGM. Meanwhile, the generalization ability of deep learning-based models is still low for the real application at recent time.
- 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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DEEP LEARNING-BASED STEREO MATCHING FOR HIGH-RESOLUTION SATELLITE IMAGES: A COMPARATIVE EVALUATION ; volume:XLVIII-1/W2-2023 ; year:2023 ; pages:1635-1642 ; extent:8
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-1/W2-2023 (2023), 1635-1642 (gesamt 8)
- Urheber
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He, X.
Jiang, S.
He, S.
Li, Q.
Jiang, W.
Wang, L.
- DOI
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10.5194/isprs-archives-XLVIII-1-W2-2023-1635-2023
- URN
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urn:nbn:de:101:1-2023122103361339815338
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:25 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- He, X.
- Jiang, S.
- He, S.
- Li, Q.
- Jiang, W.
- Wang, L.
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