Due to the fact that water surface covers wide areas, remote sensing is the most appropriate way of getting information
about ocean environment for vessel tracking, security purposes, ecological studies and others. Processing of synthetic
aperture radar (SAR) images is extensively used for control and monitoring of the ocean surface. Image data can be
acquired from Earth observation satellites, such as TerraSAR-X, ERS, and COSMO-SkyMed. Thus, SAR image
processing can be used to solve many problems arising in this field of research. This paper discusses some of them
including ship detection, oil pollution control and ocean currents mapping. Due to complexity of the problem several
specialized algorithm are necessary to develop. The oil spill detection algorithm consists of the following main steps:
image preprocessing, detection of dark areas, parameter extraction and classification. The ship detection algorithm
consists of the following main steps: prescreening, land masking, image segmentation combined with parameter
measurement, ship orientation estimation and object discrimination. The proposed approach to ocean currents mapping is
based on Doppler's law. The results of computer modeling on real SAR images are presented. Based on these results it is
concluded that the proposed approaches can be used in maritime applications.
In this paper image-based collision avoidance algorithm that provides detection of nearby aircraft and distance estimation is presented. The approach requires a vision system with a single moving camera and additional information about carrier’s speed and orientation from onboard sensors. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The proposed algorithm is able to detect small targets but unlike many other approaches is designed to work with large-scale objects as well. To localize aerial vehicle position the system of equations relating object coordinates in space and observed image is solved. The system solution gives the current position and speed of the detected object in space. Using this information distance and time to collision can be estimated. Experimental research on real video sequences and modeled data is performed. Video database contained different types of aerial vehicles: aircrafts, helicopters, and UAVs. The presented algorithm is able to detect aerial vehicles from several kilometers under regular daylight conditions.