Irregularly spaced 3D point clustering / segmentation
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
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