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Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-9692-8636
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0002-1135-4192
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1369-3216
2020 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 12, no 1, article id 76Article in journal (Refereed) Published
Abstract [en]

Mapping Earth's surface and its rapid changes with remotely sensed data is a crucial task to understand the impact of an increasingly urban world population on the environment. However, the impressive amount of available Earth observation data is only marginally exploited in common classifications. In this study, we use the computational power of Google Earth Engine and Google Cloud Platform to generate an oversized feature set in which we explore feature importance and analyze the influence of dimensionality reduction methods to object-based land cover classification with Support Vector Machines. We propose a methodology to extract the most relevant features and optimize an SVM classifier hyperparameters to achieve higher classification accuracy. The proposed approach is evaluated in two different urban study areas of Stockholm and Beijing. Despite different training set sizes in the two study sites, the averaged feature importance ranking showed similar results for the top-ranking features. In particular, Sentinel-2 NDVI, NDWI, and Sentinel-1 VV temporal means are the highest ranked features and the experiment results strongly indicated that the fusion of these features improved the separability between urban land cover and land use classes. Overall classification accuracies of 94% and 93% were achieved in Stockholm and Beijing study sites, respectively. The test demonstrated the viability of the methodology in a cloud-computing environment to incorporate dimensionality reduction as a key step in the land cover classification process, which we consider essential for the exploitation of the growing Earth observation big data. To encourage further research and development of reliable workflows, we share our datasets and publish the developed Google Earth Engine and Python scripts as free and open-source software.

Place, publisher, year, edition, pages
MDPI , 2020. Vol. 12, no 1, article id 76
Keywords [en]
EO big data, SAR, MSI, Google Earth Engine, object-based classification
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-271309DOI: 10.3390/rs12010076ISI: 000515391700076Scopus ID: 2-s2.0-85079607757OAI: oai:DiVA.org:kth-271309DiVA, id: diva2:1420548
Note

QC 20200331

Available from: 2020-03-31 Created: 2020-03-31 Last updated: 2020-03-31Bibliographically approved

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Nascetti, AndreaYousif, Osama A.Ban, Yifang

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