Development of a knowledge driven rule set for classification of submerged aquatic vegetation (SAV) in a clear water stream

Abstract

A recent attempt at mapping submerged aquatic vegetation (SAV) species composition of a clear water stream in Belgium from ultrahigh resolution, multispectral photographs, using object based image analysis (OBIA), resulted in a low, but consistent overall classification accuracy (53-61%). Since the results were obtained with a single rule set they show promise for the development of an automated tool to map SAV despite the challenges of its submerged environment. This extended abstract investigates to what extent difficulties with species delineation in the validation data may have influenced the results. We compare class boundaries, as drawn by experts along image segmentation outlines, with the results from the expert knowledge driven classification rules. A comparison for ‘pure’ objects, where the expert is certain about the assigned object class, resulted in a moderately good overall similarity (68%), while inclusion of ambiguous objects reduces the results to 59%. Under ideal circumstances the rule set seems capable of 74% similarity with expert validation data.