Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression

Johannes Rosentreter, Ron Hagensieker, Akpona Okujeni, Ribana Roscher, Paul D. Wagner, Bjorn Waske

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Hyperspectral remote sensing data offer the opportunity to map urban characteristics in detail. Though, adequate algorithms need to cope with increasing data dimensionality, high redundancy between individual bands, and often spectrally complex urban landscapes. The study focuses on subpixel quantification of urban land cover compositions using simulated environmental mapping and analysis program (EnMAP) data acquired over the city of Berlin, utilizing both machine learning regression and classification algorithms, i.e., multioutput support vector regression (MSVR), standard support vector regression (SVR), import vector machine classifier (IVM), and support vector classifier (SVC). The experimental setup incorporates a spectral library and a reference land cover fraction map used for validation purposes. The library spectra were synthetically mixed to derive quantitative training data for the classes vegetation, impervious surface, soil, and water. MSVR and SVR models were trained directly using the synthetic mixtures. For IVM and SVC, a modified hyperparameter selection approach is conducted to improve the description of urban land cover fractions by means of probability outputs. Validation results demonstrate the high potential of the MSVR for subpixel mapping in the urban context. MSVR outperforms SVR in terms of both accuracy and computational time. IVM and SVC work similarly well, yet with lower accuracies of subpixel fraction estimates compared to both regression approaches.

Original languageEnglish
Article number7836299
Pages (from-to)1938-1948
Number of pages11
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume10
Issue number5
DOIs
StatePublished - May 2017
Externally publishedYes

Keywords

  • Import vector machine (IVM)
  • Machine learning
  • Multioutput support vector regression (MSVR)
  • Subpixel
  • Support vector classifier (SVC)
  • Support vector regression (SVR)
  • Urban

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