In tommasojulitta/FieldSpectroscopyCC: R package for Characterization and Calibration of spectrometers

An Example vignette with demonstrations

library(knitr)
opts_chunk$set(out.extra='style="display:block; margin: auto"'
    #, fig.align="center"
    , fig.width=4.3, fig.height=3.2, dev.args=list(pointsize=10)
    )
knit_hooks$set(spar = function(before, options, envir) {
    if (before){
        par( las=1 )                   #also y axis labels horizontal
        par(mar=c(2.0,3.3,0,0)+0.3 )  #margins
        par(tck=0.02 )                          #axe-tick length inside plots             
        par(mgp=c(1.1,0.2,0) )  #positioning of axis title, axis labels, axis
     }
})

Introduction

TODO Some general explantion of the task and setup that hte package addresses.

Example data

packageDir <- system.file(package='FieldSpectroscopyCC')    # only works if package has been installed
dataDir <- file.path(packageDir,"data")
load(file.path(dataDir,"outdoor_rad_cal_data.Rdata"))
load(file.path(dataDir,"atmospheric_absorption_regions.Rdata"))
integration_time<-450
str( outdoor_rad_cal_data, max.level = 1 )

TODO explain the example data.

TODO shorten name of dataset, to shorten code samples.

TODO use more descriptive names inside the dataset.

Processing

First, the raw measurement need to be devided by integration time, and several spectra are summarized to a mean spectrum.

DN_mat<-DN_spectral_matrix_for_rad_cal(outdoor_rad_cal_data$DN_matrix,integration_time=integration_time)
radiance_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$radiance_wl,spectra=outdoor_rad_cal_data$radiance_matrix,fun='mean')
str(radiance_mean)

For the calibration, the spectrum needs to be available at given reference wavelength, which are linearly interpolated from the mean spectrum.

TODO: What is $wp_coef?

radiance_mean_res<-linear_resample(outdoor_rad_cal_data$radiance_wl,radiance_mean,outdoor_rad_cal_data$DN_wl)
wp_coeff_res<-linear_resample(outdoor_rad_cal_data$wp_coef$V1,outdoor_rad_cal_data$wp_coef$V2,outdoor_rad_cal_data$DN_wl)
DN_mat<-DN_spectral_matrix_for_rad_cal(outdoor_rad_cal_data$DN_matrix,integration_time=integration_time)
radiance_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$radiance_wl,spectra=outdoor_rad_cal_data$radiance_matrix,fun='mean')
str(radiance_mean)

TODO explain the other things Thomas did not understand it fully yet, what is radiance compared to DN?

#calculate mean of several spectra
DN_mean<-StatsOnSpectra(wl=outdoor_rad_cal_data$DN_wl,spectra=outdoor_rad_cal_data$DN_matrix,fun='mean')
#calculate calibration coefficients
rad_cal<-radiometric_calibration(type=1,wl=outdoor_rad_cal_data$DN_wl,radiance_mean_res,DN_mean)
#exclude regions of the spectrum affected by atmospheric absorptions and noisy pixels
range_to_exclude<-data.frame(wl_start=c(outdoor_rad_cal_data$DN_wl[1],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)-35]),wl_end=c(outdoor_rad_cal_data$DN_wl[30],outdoor_rad_cal_data$DN_wl[length(outdoor_rad_cal_data$DN_wl)]))
atmospheric_absorption_regions<-rbind(atmospheric_absorption_regions,c(range_to_exclude))
exclude_atmospheric_absorption_features<-exclude_spectral_regions(wl=outdoor_rad_cal_data$DN_wl,rad_cal,atmospheric_absorption_regions)
#smooth results
rad_cal_coeff<-spline_smooth_gapfilling(wl=outdoor_rad_cal_data$DN_wl,exclude_atmospheric_absorption_features) 

plot(outdoor_rad_cal_data$DN_wl,rad_cal,ylim=c(0,0.000025))
points(outdoor_rad_cal_data$DN_wl,exclude_atmospheric_absorption_features,pch=20,col="red")
lines(outdoor_rad_cal_data$DN_wl,rad_cal_coeff,col="green",lwd=2)
lines(outdoor_rad_cal_data$DN_wl,rad_cal_edge_coef,col="dark green",lwd=2)

tommasojulitta/FieldSpectroscopyCC documentation built on May 31, 2019, 6:19 p.m.