The Mondrian detection algorithm for sonar imagery

Abstract

A new algorithm called the Mondrian detector has been developed for object detection in high-frequency synthetic aperture sonar (SAS) imagery. If a second (low) frequency-band image is available, the algorithm can seamlessly exploit the additional information via an auxiliary prescreener test. This flexible single-band and multiband functionality fills an important capability gap. The algorithm's overall prescreener component limits the number of potential alarms. The main module of the method then searches for areas that pass a subset of pixel-intensity tests. A new set of reliable classification features has also been developed in the process. The overall framework has been kept uncomplicated intentionally in order to facilitate performance estimation, to avoid requiring dedicated training data, and to permit delayed real-time detection at sea on an autonomous underwater vehicle. The promise of the new algorithm is demonstrated on six substantial data sets of real SAS imagery collected at various geographical sites that collectively exhibit a wide range of diverse seafloor characteristics. The results show that, as with Mondrian's art, simplicity can be powerful.