.png){kind=icon}
.png){kind=icon}
Return
A three-level Multiple-Kernel Learning approach for soil spectral analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2020.01.008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
To ensure the sustainability of the soil ecosystem, which is the basis for food production, efficient large-scale baseline predictions and trend assessments of key soil properties are necessary. In that regard, visible, near-infrared, and shortwave infrared (VNIR-SWIR) spectroscopy can provide an alternative for the expensive wet chemistry. In this paper, we examined the application of the Multiple-Kernel Learning (MKL) approach to soil spectroscopy by integrating the information from heterogeneous features. In particular, the proposed three-level MKL framework acts in the following way: at the first level, it uses multiple kernels at each spectral feature (wavelength) to maximize the information of each band. At the second level, it performs implicit feature selection at the spectral source level, enabling it to provide interpretable results. Finally, at the third level of integration it combines the complementary information contained within a pool of spectral sources, each derived from its own set of pre-processing techniques. Additionally, at this stage, the proposed approach is also capable of fusing heterogeneous sources of information, such as auxiliary predictors, which can assist the spectral predictions. The experimental analysis was conducted using the pan-European LUCAS (Land Use/Cover Area frame statistical Survey) topsoil database, with a goal to predict from the VNIR-SWIR spectra the concentration of soil organic carbon (SOC), a key indicator for agricultural productivity and environmental resilience. The particle size distribution which describes the soil texture was selected as the set of auxiliary predictors. The proposed MKL framework was compared with other state-of-the-art approaches, and the results indicated that it attains the best performance in terms of accuracy, whilst at the same time producing interpretable results. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Multiple Kernel Learning (MKL)
Kernel alignment
Heterogeneous source combination
Soil organic carbon
Soil texture
VNIR-SWIR spectroscopy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W