File:An effective noise filtering method for mine detection (IA aneffectivenoise109455576).pdf

Automatic detection of sea mines in coastal regions is difficult due to highly varying sea bottom conditions present in the underwater environment. Detection systems must be able to discriminate objects that vary in size, shape, and orientation from naturally occurring and man-made clutter. Additionally, these automated systems must be computationally efficient to be incorporated into Unmanned Aerial Vehicle (UAV) sensor systems characterized by high sensor data-rates and limited processing abilities. Commonly used noise filters largely depend on the window (or neighborhood) size, which makes the mine detection ineffective. Using the bi-dimensional empirical mode decomposition (BEMD) analysis, an effective, robust sea mine detection system can be created. A family of decomposed images is generated and applied to optical lidar image data from the Rapid, Overt, Airborne, Reconnaissance (ROAR) experiment supplied by Naval Surface Warfare Center, Panama City. These decompositions project key image features, geometrically defined structures with orientations, and localized information into distinct orthogonal components or feature subspaces of the image. Application of the BEMD method to the analysis on side scan sonar data is also provided. Accurate detection and classification of mines is time consuming and requires divers or Autonomous Underwater Vehicles (AUV) in the water. The navy continues to pursue more expedient methods in mine countermeasures, and with airborne lidar, a surf zone (SZ) and landing zone can be quickly surveyed for possible mines. In the near surf zone, all possible mines can be quickly neutralized by dropping guided munitions, eliminating the need to send divers or AUVs to verify contacts. Still, the need for improved methods of detection and classification is needed. BEMD, a relatively new method of signal analysis developed about fifteen years ago, was tested on lidar imagery from the ROAR experiment to look for any improvements in detecting and classifying mines.