Sonar object discrimination via spectral density analysis
Current underwater sonar exploration often neglects the information that exists in the spectral density of object returns. The attempt to perform spectral density exploration of objects is complicated by the difficulty to present informative spectral content of each location pixel on a 2D image. Spectral content is best represented by the spectral density; however, it does not make sense to explore the detailed spectral density for each pixel in the image. In this work, we outline a general approach for spectral density analysis which is intended for enhancing object discrimination and delivers an easy to interpret, image enhancement of sonar returns. This method actually enhances the sonar image with acoustic color which emphasizes an optimal combination of frequency bands of the returned spectrum for the purpose of object discrimination. This is achieved by analyzing the discriminating power of different frequency bands and creating an optimal association with different bands to a corresponding color map.