waveformlidar (version 1.2.0)

decom: decom

Description

The function allows you to eatimate parameters charcterizing waveforms and to pave the way for generating waveform-based point cloud.

Usage

decom(x, smooth = TRUE, width = 3, thres = 0.22)

Arguments

x

is a waveform with a index at the begining and followed with intensities.

smooth

is tell whether you want to smooth the waveform to remove some obvious outliers. Default is TRUE.

width

width of moving window.Default is 3, must be odd integer between 1 and n.This parameter ONLY work when the smooth is TRUE.

thres

is to determine if the detected peak is the real peak whose intensity should be higher than threshold*maximum intensity. Default is 0.22.

Value

A list contains estimates of A, u, sig after decomposition.

References

Zhou, Tan*, Sorin C. Popescu, Keith Krause, Ryan D. Sheridan, and Eric Putman, 2017. Gold-A novel deconvolution algorithm with optimization for waveform LiDAR processing. ISPRS Journal of Photogrammetry and Remote Sensing 129 (2017): 131-150. https://doi.org/10.1016/j.isprsjprs.2017.04.021

Examples

# NOT RUN {
##import return waveform data
library(data.table)
data(return)
return<-data.table(index=c(1:nrow(return)),return)
x<-return[1,] ###must be a dataset including intensity with index at the beginning.
r1<-decom(x)
r2<-decom(x,smooth=TRUE,width=5) ###you can assign different smooth width for the data
###when it comes very noisy waveforms, it may give you some problems
xx<-return[182,]
r3<-decom(xx)  ##this one returns NULL which means the function didn't work for the
               ##complex waveform or too noisy waveform,we should try to reprocess
               ##these unsucessful waveforms using larger width to smooth the waveforms.
r4<-decom(xx,smooth=TRUE,width=5) ##when you change to a larger width, it can work,
                                  #but give you some unreasonable estimates, return NA

###original result from this decom is (you will not see it, the function filter this result
###and put NA for the estimation since they maybe not right results)
#Nonlinear regression model
#model:y~A1*exp(-(x-u1)^2/(2*sigma1^2))+A2*exp(-(x-u2)^2/(2*sigma2^2)) n\
#+A3*exp(-(x-u3)^2/(2*sigma3^2))
#data: df
#A1      A2      A3      u1      u2      u3  sigma1  sigma2  sigma3
#228.709 -30.883  81.869  41.640  42.131  71.680  14.613   3.522   8.073
##A (ampilitude should not be negative)

r5<-decom(xx,width=10) ##this will work by smoothing the waveform
r6<-decom(xx,thres=0.1,width=5)  ##by adjusting width and thres of real peak, you may
                                 ##get a reasonable results
# }
# NOT RUN {
# for the whole dataset
dr<-apply(return,1,decom)
# }
# NOT RUN {
# }