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Author (up) Rosentreter, J.; Hagensieker, R.; Okujeni, A.; Roscher, R.; Wagner, P. D.; Waske, B. doi  openurl
  Title Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression Type Journal Article
  Year 2017 Publication IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Abbreviated Journal  
  Volume PP Issue 99 Pages 1-11  
  Keywords Land surface Libraries Remote sensing Static VAr compensators Support vector machines Urban areas Vegetation mapping Import vector machine (IVM) machine learning multioutput support vector regression (MSVR) subpixel support vector classifier (SVC) support vector regression (SVR) urban  
  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.  
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  ISSN 1939-1404 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number geomatics @ Serial 531  
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