Sand grain analysis—image processing, textural algorithms and neural nets

• Published in: Computers & geosciences Vol. 24; no. 2; pp. 111 - 118
• Main Authors: Williams, A.T, Wiltshire, R.J, Thomas, M.C
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.1998; Elsevier Science
• Subjects: Earth sciences; Earth, ocean, space; Exact sciences and technology; Petrology of sedimentary rocks except quaternary rocks; Sand grain, Image analysis, Textural analysis, Statistical classification; Sedimentary rocks; New York; United States; Southern Africa; Namibia; Fire Island; Brazil; North Atlantic; Northwest Atlantic; Sand grain, Image analysis, Textural analysis, Statistical classification; South America; textures; Atlantic Ocean; Africa; silicates; quartz; framework silicates; North America; image analysis; grains
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/S0098-3004(98)00004-1

A quantitative method for characterising and classifying quartz grain form by mathematical analysis of surface texture is presented. Scanning electron images of quartz grains were ‘frame grabbed’ and converted to a digitised grey level image. Image enhancement, segmentation and histogram equalisation were applied to produce standardised images. Two major textural approaches were then applied. The Roberts gradient operator determined the degree of surface edgeness while calculation of spatial grey level dependence matrices allowed production of distribution maps of surface homogeneity, entropy and correlation. Textural parameters were obtained from samples of 0.5 mm quartz grains from three distinct populations: Desert quartz; Fire Island, New York, beach grains; and Brazilian crushed quartz. A comparative analysis using discriminant analysis and neural networks was undertaken to quantify the success in classifying the different populations. Both methods achieved excellent degree of quartz grain classification. However the use of neural networks provided a more robust method of analysis particularly when presented with incomplete data sets.