This paper describes and tests a method for the automated extraction of
grain-size data from digital imagery. It combines two basic image
processing methods for this purpose: grey-scale thresholding to create
a binary image and watershed segmentation to grow edges on the binary
image to allow the identification of individual grains. The method is
subject to rigorous testing in terms of edge detection and automatic
measurement of grain-size information from the edge images, and is also
compared with the results obtained from simple direct clast sampling.
The edge detection methods are tested with respect to
manually-identified edges. This suggests that simple thresholding of
raw imagery produces grain-size estimates that are: (i) in excellent
agreement with manual estimates, above a critical particle size defined
by the scale of the photography; (ii) downgraded with the inclusion of
additional edge information from analysis of high resolution digital
elevation models (DEMs); and (iii) not affected by the use of raw
imagery as opposed to imagery that has been rectified to deal with
geometric, tilt and relief distortion effects. The automated
ellipse-based measurement method is shown to produce a good estimate of
two-dimensional a- and b- axes as they appear as long and short axes on
the edge images. Thus, the research shows that it can be used to map
and quantify very rapidly spatial variations in grain-size
characteristics, although it cannot deal with the long-recognised
problem of the relationship between two-dimensional planform grain-size
estimates and actual a- and b- axes obtained by direct grain sampling.