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Automated Grain Sizing for Fluvial Gravels

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Graham DJ, Reid I and Rice SP. 2005. Automated sizing of coarse-grained sediments: image-processing procedures. Mathematical Geology 37(1): 1:28.


This is the first in a pair of papers in which we present image-processing-based procedures for the measurement of fluvial gravels. The spatial and temporal resolution of surface grain-size characterization is constrained by the time-consuming and costly nature of traditional measurement techniques. Several groups have developed image-processing-based procedures, but none have demonstrated the transferability of these techniques between sites with different lithological, clast form and textural characteristics. Here we focus on image-processing procedures for identifying and measuring image objects (i.e. grains); the second paper examines the application of such procedures to the measurement of fluvially deposited gravels. Four image-segmentation procedures are developed, each having several internal parameters, giving a total of 416 permutations. These are executed on 39 images from three field sites at which the clasts have contrasting physical properties. The performance of each procedure is evaluated against a sample of manually digitized grains in the same images, by comparing three derived statistics. The results demonstrate that it is relatively straightforward to develop procedures that satisfactorily identify objects in any single image or a set of images with similar sedimentary characteristics. However, the optimal procedure is that which gives consistently good results across sites with dissimilar sedimentary characteristics. We show that neighborhood-based operations are the most powerful, and a morphological bottom-hat transform with a double threshold is optimal. It is demonstrated that its performance approaches that of the procedures giving the best results for individual sites. Overall, it out-performs previously published, or improvements to previously published, methods.

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Graham DJ, Rice SP and Reid I. 2005. A transferable method for the automated grain sizing of river gravels. Water Resources Research 41(W07020): doi:10.1029/2004WR003868.


The spatial and temporal resolution of surface grain-size characterization is constrained by the limitations of traditional measurement techniques. In this paper we present an extremely rapid image-processing-based procedure for the measurement of exposed fluvial gravels and other coarse-grained sediments, defining the steps required to minimize the errors in the derived grain-size distribution. This procedure differs significantly from those used previously. It is based around a robust object-detection algorithm that produces excellent results on images exhibiting a wide range of sedimentary conditions, crucially, without any user intervention or site-specific parameterization. The procedure is tested using a dataset comprising 39 images from three rivers with contrasting grain lithology, shape, roundness and packing configuration and representing a very wide range of textures. It is shown to perform more consistently than the best existing automated method, achieving a precision equivalent to that obtainable by Wolman sampling, but taking between one sixth and one twentieth of the time. The error in area-by-number grain-size distribution percentiles is typically less than 0.05 psi.

Copyright 2005 American Geophysical Union. Further reproduction or electronic distribution is not permitted.

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