A hierarchical classification system for object recognition in underwater environments

In this paper, a hierarchical system, in which each level is composed by a neural-based classifier, is proposed to recognize objects in underwater images. The system has been designed to help an autonomous underwater vehicle in sea-bottom survey operations, like pipeline inspections. The input image is divided into square regions (macro-pixels) and a neural tree is used to classify each region into different object classes (pipeline, sea-bottom, or anodes). Each macro-pixel is then analyzed according to some geometric and environment constraints: macro-pixels with doubt classification are divided into four parts and re-classified. The process is iterated until the desired accuracy is reached. Experimental results, which have been performed on a large set of real underwater images acquired in different sea environments, demonstrate the robustness and the accuracy of the proposed system.