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Hafner et al., 2022 - Google Patents

Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data

Hafner et al., 2022

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Document ID
10388046962388540351
Author
Hafner S
Ban Y
Nascetti A
Publication year
Publication venue
Remote Sensing of Environment

External Links

Snippet

Accurate and up-to-date maps of built-up areas are crucial to support sustainable urban development. Earth Observation (EO) is a valuable data source to cover this demand. In particular, Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral …
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Classifications

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    • G—PHYSICS
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    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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