waveformlidar (version 1.2.0)

decom.adaptive: decom.adaptive

Description

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

Usage

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

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.

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.

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.

Value

A list contains estimates of A, u, sig and ri (rate parameter in adaotive Gaussian function) after decomposition.

References

Tan Zhou, and Sorin C. Popescu, 2017. Bayesian decomposition of full waveform LiDAR data with uncertainty analysis. Remote Sensing of Environment 200 (2017): 43-62.

Examples

# NOT RUN {
##import return waveform data
data(return)
lr<-nrow(return)
ind<-c(1:lr)
return<-data.frame(ind,return)
x<-return[1,] ###must be a dataset including intensity with index at the beginning.
r1<-decom(x)

r2<-decom.adaptive(x)

# }
# NOT RUN {
# for the whole dataset
dr3<-apply(return[50:200, ],1,decom.adaptive)
# }