Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection |
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Authors: | Thorsten Mewes Jonas Franke Gunter Menz |
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Institution: | 1.Center for Remote Sensing of Land Surfaces (ZFL),University of Bonn,Bonn,Germany;2.RSS—Remote Sensing Solutions GmbH,München,Germany;3.Remote Sensing Research Group (RSRG), Department of Geography,University of Bonn,Bonn,Germany |
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Abstract: | 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. |
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