期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 10, 期 4, 页码 1640-1650出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2016.2634859
关键词
Ensemble learning; hyperspectral; imaging spectrometry; machine learning; subpixel mapping; support vector regression (SVR); urban remote sensing
类别
资金
- Belgian Federal Science Policy (Belspo) as part of the UrbanEARS project within the Research Programme for Earth Observation-Stereo III [SR/00/307]
- Federal Ministry of Research and Education (BMBF) [FKZ 01LK0901A]
- German Federal Ministry of Economics and Technology (BMWi) [FKZ 01LK0901A]
Generating synthetically mixed data from library spectra provides a direct means to train empirical regression models for subpixel mapping. In order to best represent the subpixel composition of image data, the generation of synthetic mixtures must incorporate a multitude of mixing possibilities. This can lead to an excessive amount of training samples. We show that increasing mixing complexity in the training set improves model performance when quantifying urban land cover with support vector regression (SVR). To cope with the challenging increase in the number of training samples, we propose the use of ensemble learning based on bootstrap aggregation from synthetically mixed training data. The workflow is tested on simulated spaceborne imaging spectrometer data acquired over Berlin, Germany. Comparisons to SVR without bagging and multiple endmember spectral mixture analysis reveal the usefulness of the methodology for quantitative urban mapping.
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