Irregularly spaced 3D point clustering /segmentation

library(rgdal)
library(trip)  ## just for my oc.colors function  - any color function 
will do

z <- d$elev - d$minElev  ## colourize based on height above ground

## this is taken from surface3d
z <- z - min(z)
zlim <- range(z)
zlim <- zlim - min(zlim)
zlen <- zlim[2] - zlim[1] + 1
colorlut <- oc.colors(zlen) # height color lookup table
col <- colorlut[z -zlim[1]+1 ] # assign colors to heights for each point
   
## subsample it for sanity until you are happy it's right
 sub <- sample(1:nrow(d), 5000) 
## sub <- 1:nrow(d)
 plot3d(d[sub,1], d[sub,2], d[sub, 3], aspect = "iso", col = col, size = 3)

Sample images here:
http://staff.acecrc.org.au/~mdsumner/R/lidar.png
http://staff.acecrc.org.au/~mdsumner/R/lidar2.png

That might help you with your clustering explorations.

Cheers, Mike.

Andrew Niccolai wrote:
  

Absolutely.  I am waiting on a LIDAR data set for my research site and
thought that I would get a head start with building the code to segment the
data. So, I downloaded the US Forest Service free software FUSION that was
built to visualize LIDAR data.  This software comes with an example set from
the Pacific Northwest including an orthophoto of the same site.  This is the
data that I am currently using while awaiting my own.

Hope this helps.  Let me know if you end up making any headway with
separating out the trees....

cheers

Andrew Niccolai
Doctoral Candidate
Yale University
(203) 432-5144
-----Original Message-----
From: Michael Sumner [mailto:mdsumner at utas.edu.au] 
Sent: Thursday, May 17, 2007 5:33 PM
To: Andrew Niccolai
Cc: r-sig-geo at stat.math.ethz.ch
Subject: Re: [R-sig-Geo] Irregularly spaced 3D point clustering /
segmentation

Hello,

 I have no suggestions for helping yet, but I'd rather like to play with 
some LIDAR forest data. Is there some publicly available that you can 
point me to?  I have messed around with rgl for interactive 3-D view of 
similar things, and would like to explore more.

Cheers, Mike.

Andrew Niccolai wrote:
  
    

Greetings fellow R users,

I would really enjoy (and eagerly anticipate) any discussions on ideas for
handling a LIDAR (laser) data set of a New England forest.  The LIDAR
dataset is essentially xyz coordinates that form an irregularly spaced 3D
data cloud of points.  I have brought the data in as
    
      

SpatialPointsDataFrame,
  
    

SpatialPixelsDataFrame, SpatialGridDataFrame, marked Point Pattern Process
objects, matrices etc.  I can view the interpolated surface with ?interp
    
      

in
  
    

library(akima) as well as 3D points and surfaces in library(rgl).

So, importing the LIDAR data and viewing it or exporting it so that ImageJ
can handle it is not the issue.  

The LIDAR data set essentially produces a set of "mounds" from the
    
      

elevation
  
    

data recorded in the z variable.  Each "mound" represents a tree in the
forest.  I am hoping to get some ideas on ways to cluster this data set so
that I can isolate each mound for further analysis and segmentation.  One
possibility that I have looked into with Matlab software is
"marker-controlled watershed segmentation".  This essentially inverts the
interpolated surface and "fills" the inverted image with "water" starting
    
      

at
  
    

the local minimas until the water starts to spill over into the next
watershed at which point it builds a "dam" between local valleys.  This is
    
      

a
  
    

function in Matlab and I haven't been able to see the code to bring it to
    
      

R.
  
    

Any ideas on this method or suggestions for better methods to isolate
"mounds" in 3D space?  Template matching, perhaps??

Thanks in advance and thanks to all the innovative producers and users of
the R domain!!  
Andrew