Classification performance assessment in side scan sonar image while underwater target object recognition using random forest classifier and support vector machine

Ocean mine have been a major threat to the safety of vessels and human lives for many years. Identification of mine-like objects is a pressing need for military, and other ocean meets. In mine, countermeasures operations, sonar equipment are utilised to detect and classify mine- like objects if their sonar signatures are similar to known signatures of mines. The classification of underwater mines is an important  task,  for  the  safety of  ports,  harbors  and  the  open  sea.  Mine detection is needed in military applications because it has been a threat to many lives and vessels. Although the task of finding mine like objects has received recent attention, very little has been published on the problem of discriminating mine-like (target) objects (MLO) and non-mine like (target) objects of similar size and shape. This paper deals with the recognition of mine like and non mine like objects. The recognition is done through robust Random Forest technique.