DOI: 10.1016/j.jag.2016.07.016
Scopus记录号: 2-s2.0-84997822665
论文题名: Spectral band selection for vegetation properties retrieval using Gaussian processes regression
作者: Verrelst J ; , Rivera J ; P ; , Gitelson A ; , Delegido J ; , Moreno J ; , Camps-Valls G
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 554
结束页码: 567
语种: 英语
英文关键词: ARTMO
; Band selection
; Gaussian processes regression (GPR)
; Hyperspectral
; Machine learning
; Vegetation properties
Scopus关键词: algorithm
; Gaussian method
; machine learning
; regression analysis
; spectral analysis
; vegetation index
; Glycine max
; Zea mays
英文摘要: With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables. This paper introduces an automated spectral band analysis tool (BAT) based on Gaussian processes regression (GPR) for the spectral analysis of vegetation properties. The GPR-BAT procedure sequentially backwards removes the least contributing band in the regression model for a given variable until only one band is kept. GPR-BAT is implemented within the framework of the free ARTMO's MLRA (machine learning regression algorithms) toolbox, which is dedicated to the transforming of optical remote sensing images into biophysical products. GPR-BAT allows (1) to identify the most informative bands in relating spectral data to a biophysical variable, and (2) to find the least number of bands that preserve optimized accurate predictions. To illustrate its utility, two hyperspectral datasets were analyzed for most informative bands: (1) a field hyperspectral dataset (400–1100 nm at 2 nm resolution: 301 bands) with leaf chlorophyll content (LCC) and green leaf area index (gLAI) collected for maize and soybean (Nebraska, US); and (2) an airborne HyMap dataset (430–2490 nm: 125 bands) with LAI and canopy water content (CWC) collected for a variety of crops (Barrax, Spain). For each of these biophysical variables, optimized retrieval accuracies can be achieved with just 4 to 9 well-identified bands, and performance was largely improved over using all bands. A PROSAIL global sensitivity analysis was run to interpret the validity of these bands. Cross-validated RCV 2 (NRMSECV) accuracies for optimized GPR models were 0.79 (12.9%) for LCC, 0.94 (7.2%) for gLAI, 0.95 (6.5%) for LAI and 0.95 (7.2%) for CWC. This study concludes that a wise band selection of hyperspectral data is strictly required for optimal vegetation properties mapping. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80042
Appears in Collections: 气候变化事实与影响
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作者单位: Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València, Spain; Departamento de Oceanografía Física, CICESE, Ensenada, Mexico; Israel Institute of Technology, Technion, Haifa, Israel
Recommended Citation:
Verrelst J,, Rivera J,P,et al. Spectral band selection for vegetation properties retrieval using Gaussian processes regression[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52