Crop separability from individual and combined airborne imaging spectroscopy and UAV multispectral data
| Autor: | Michael E. Schaepman, Jonas E. Böhler, Mathias Kneubühler |
|---|---|
| Přispěvatelé: | University of Zurich, Böhler, Jonas E |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Multispectral data
UFSP13-8 Global Change and Biodiversity Science 1900 General Earth and Planetary Sciences imaging spectroscopy Crop species Random forest Imaging spectroscopy crop separability 10122 Institute of Geography Feature (computer vision) band reduction Range (statistics) Environmental science RGB color model General Earth and Planetary Sciences Prism 910 Geography & travel multispectral drone data random forest Remote sensing |
| Zdroj: | Remote Sensing Volume 12 Issue 8 Remote Sensing, Vol 12, Iss 1256, p 1256 (2020) |
| DOI: | 10.5167/uzh-188818 |
| Popis: | Crop species separation is essential for a wide range of agricultural applications&mdash in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural areas, very high spatial resolution data (VHR) are required. We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX). Both the single usage and combination of these datasets were analyzed using a random forest-based method for crop separability. In addition, different band reduction methods based on feature factor loading were analyzed. The most accurate crop separation results were achieved using both the IS dataset and the two combined datasets with an average accuracy (AA) of > 92%. In addition, we conclude that, in the case of a reduced number of IS features (i.e., wavelengths), the accuracy can be compensated by using additional NIR-RGB texture features (AA > 90%). |
| Databáze: | OpenAIRE |
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