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.
Many researchers have been done in classifying the surface of the sea floor. Only few concentrated in classifying the sediment layers of the sea floor and the target objects buried. Side Scan Sonar is one such tool, which is used in collecting the images of the seafloor. Sonar equipment transmits a low frequency signal, towards the surface of the seabed for target recognition. It is necessary to locate the area and positioning where the target is located whether it is a ship wreckage or plane crash, mine recognition etc. This paper is proposed to determine the location and position in user friendly matlab software environment, where the sonar image data is collected and is mapped with Global Reference System.
The resolution of the side scan sonar image which is used to detect on seabed such as mines, ship wrecks, etc is low. This paper helps to utilizes image processing techniques to enhance the resolution and thereby it makes detection and classification of underwater objects accurately. The proposed methods discussed in this paper are Discrete wavelet transform and stationary wavelet transform for enhancing the resolution.
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