A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results
| Autor: | Marco Scavazzon, Vasileios Syrris, Luigi Zanchetta, Pierre Soille, Linlin Lu, Georgios K. Ouzounis, Daniele Ehrlich, Thomas Kemper, Martino Pesaresi, Guo Huadong, Mayeul Kauffmann, Matina Halkia, Xavier Blaes, Lionel Gueguen, Mario A. Marin-Herrera, Stefano Ferri |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
PANTEX
Atmospheric Science education.field_of_study Contextual image classification Computer science QC801-809 Feature extraction Multispectral image Population Built-up density Geophysics. Cosmic physics Image segmentation global human settlement layer Panchromatic film Ocean engineering Feature (computer vision) CSL linear regression Computers in Earth Sciences urban limits education Image resolution TC1501-1800 Remote sensing |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 6, Iss 5, Pp 2102-2131 (2013) |
| ISSN: | 2151-1535 |
| Popis: | A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted. |
| Databáze: | OpenAIRE |
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