Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging
1
Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
2
Building Safety Research Center & Seismic Safety Research Center, Korea Institute of Civil Engineering and Building Technology, Daejeon 34141, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1611; https://doi.org/10.3390/s20061611
Received: 29 January 2020
/
Revised: 10 March 2020
/
Accepted: 10 March 2020
/
Published: 13 March 2020
(This article belongs to the Special Issue Hyperspectral Imaging (HSI) Sensing and Analysis)
Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.
View Full-Text
Keywords:
granite soils; water content; hyperspectral camera; visible and near-infrared; soil water characteristic curve; artificial neural network
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Lim, H.-H.; Cheon, E.; Lee, D.-H.; Jeon, J.-S.; Lee, S.-R. Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging. Sensors 2020, 20, 1611. https://doi.org/10.3390/s20061611
AMA Style
Lim H-H, Cheon E, Lee D-H, Jeon J-S, Lee S-R. Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging. Sensors. 2020; 20(6):1611. https://doi.org/10.3390/s20061611
Chicago/Turabian StyleLim, Hwan-Hui, Enok Cheon, Deuk-Hwan Lee, Jun-Seo Jeon, and Seung-Rae Lee. 2020. "Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging" Sensors 20, no. 6: 1611. https://doi.org/10.3390/s20061611
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
