Artificial Neural Network Classification of Sand in all Visible Submarine and Subaerial Regions of a Digital Image

Abstract

Factors controlling the distribution of shelf sand as a resource, a component of reef ecosystems, and a dynamic substrate are poorly understood. An initial step in understanding sand accumulation in each of these roles is to identify its areal extent and change through time. Digitized aerial photographs and digital images provide common, inexpensive data sets that are generally underutilized for the purpose of marine substrate classification. Here we use only two bands, blue and green (470 and 550 nm), to demonstrate the utility of simple aerial photography in classifying marine substrate. Although these two are acquired from a hyperspectral data set, they represent blue and green in an RGB image such as commonly available in digitized aerial photographs. We add as a third band the second eigenchannel of a principal components analysis of these bands. Using an artificial neural network classification model, we identify submarine and subaerial sandy substrate in a digital image of a detached reef island in the Red Sea, Gezirat Siyul, Egypt. With careful selection of training and test groups, using small percentages of the total classified image, we create an efficient and accurate classification model. The model, trained to identify two classes, “sand” and “other than sand,” produces a classified image that provides sand locations and approximate areal coverage. Confusion matrices for both training and testing groups have user's accuracies in the 90 percentiles, indicating accurate pixel classification.