caa a22 4500 445864389 CHVBK 20180317145453.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s11119-011-9222-9 doi (NATIONALLICENCE)springer-10.1007/s11119-011-9222-9 Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection [Elektronische Daten] [Thorsten Mewes, Jonas Franke, Gunter Menz] Remote sensing approaches are of increasing importance for agricultural applications, particularly for the support of selective agricultural measures that increase the productivity of crop stands. In contrast to multi-spectral image data, hyperspectral data has been shown to be highly suitable for the detection of crop growth anomalies, since they allow a detailed examination of stress-dependent changes in certain spectral ranges. However, the entire spectrum covered by hyperspectral data is probably not needed for discrimination between healthy and stressed plants. To define an optimal sensor-based system or a data product designed for crop stress detection, it is necessary to know which spectral wavelengths are significantly affected by stress factors and which spectral resolution is needed. In this study, a single airborne hyperspectral HyMap dataset was analyzed for its potential to detect plant stress symptoms in wheat stands induced by a pathogen infection. The Bhattacharyya distance (BD) with a forward feature search strategy was used to select relevant bands for the differentiation between healthy and fungal infected stands. Two classification algorithms, i.e. spectral angle mapper (SAM) and support vector machines (SVM) were used to classify the data covering an experimental field. Thus, the original dataset as well as datasets reduced to several band combinations as selected by the feature selection approach were classified. To analyze the influence of the spectral resolution on the detection accuracy, the original dataset was additionally stepwise spectrally resampled and a feature selection was carried out on each step. It is demonstrated that just a few phenomenon-specific spectral features are sufficient to detect wheat stands infected with powdery mildew. With original spectral resolution of HyMap, the highest classification accuracy could be obtained by using only 13 spectral bands with a Kappa coefficient of 0.59 in comparison to Kappa 0.57 using all spectral bands of the HyMap sensor. The results demonstrate that even a few hyperspectral bands as well as bands with lower spectral resolution still allow an adequate detection of fungal infections in wheat. By focusing on a few relevant bands, the detection accuracy could be enhanced and thus more reliable information could be extracted which may be helpful in agricultural practice. Springer Science+Business Media, LLC, 2011 Feature selection nationallicence Bhattacharyya distance nationallicence Crop stress nationallicence Data reduction nationallicence Hyperspectral data nationallicence Spectral angle mapper nationallicence Spectral resolution nationallicence Support vector machines nationallicence Mewes Thorsten Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Walter-Flex-Str. 3, 53113, Bonn, Germany aut Franke Jonas RSS—Remote Sensing Solutions GmbH, Wörthstr. 49, 81667, München, Germany aut Menz Gunter Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Walter-Flex-Str. 3, 53113, Bonn, Germany aut Precision Agriculture Springer US; http://www.springer-ny.com 12/6(2011-12-01), 795-812 1385-2256 12:6<795 2011 12 11119 https://doi.org/10.1007/s11119-011-9222-9 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11119-011-9222-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Mewes Thorsten Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Walter-Flex-Str. 3, 53113, Bonn, Germany aut NATIONALLICENCE 700 1- Franke Jonas RSS—Remote Sensing Solutions GmbH, Wörthstr. 49, 81667, München, Germany aut NATIONALLICENCE 700 1- Menz Gunter Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Walter-Flex-Str. 3, 53113, Bonn, Germany aut NATIONALLICENCE 773 0- Precision Agriculture Springer US; http://www.springer-ny.com 12/6(2011-12-01), 795-812 1385-2256 12:6<795 2011 12 11119 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer