Abstract
Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1–100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU’s Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors.
Highlights
Many parties, from both public and private sectors, have the need to be aware of the presence of ships and shipping traffic at sea
For use on multi-look images, which contain only the amplitude of the radar backscatter, but not its phase, the most widely-used approach is the adaptive threshold or Constant False Alarm Rate (CFAR) detector, which looks for pixel values that are high compared to the local background (e.g., [10,11])
The Search for Unidentified Maritime Objects (SUMO) ship detector algorithm for satellite radar images is based on the classic and straightforward CFAR approach of detecting pixels that are high in relation to their local background
Summary
From both public and private sectors, have the need to be aware of the presence of ships and shipping traffic at sea. A number of systems can provide data on this, and a useful distinction can be made between cooperative/reporting systems, where ships provide data themselves, and non-cooperative/observation systems, where sensors are used to obtain data not relying on the ships’ cooperation Among the latter systems, imaging radar deployed on orbiting satellites is Remote Sens. Almost all imaging radars on satellites are of the SAR type, because only that technique leads to a high resolution at the long ranges that characterize space-based observation [1]. For use on multi-look images, which contain only the amplitude (or power) of the radar backscatter, but not its phase, the most widely-used approach is the adaptive threshold or Constant False Alarm Rate (CFAR) detector, which looks for pixel values that are high compared to the local background (e.g., [10,11]). SUMO has been used in many scientific publications, in particular on fisheries’ control [26,27,28,29,30,31], maritime surveillance [32,33,34,35,36] and benchmarking [37,38,39,40], but its detailed working has never been fully described
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