CNN-based false alarm mitigation for maritime objects detected in low resolution synthetic aperture radar imagery

Abstract

Canada’s RADARSAT-2 (R-2) low-resolution ScanSAR Detection of Vessels, Wide swath, Far incidence angle (DVWF) mode was implemented for vessel detection over wide swaths. Between 2013 and 2020, DVWF imagery was used operationally, by the Department of National Defence (DND), and exploited using a tool that detects clusters of bright pixels with vessel-like signature in the SAR imagery. The detected objects were then associated with automatic identification system (AIS) messages. Not all vessels transmit AIS messages, and not all detected vessels that transmit AIS are associated; therefore, analysts observed non-associated objects in an attempt to validate detected vessels. The new work described here evaluates the use of a convolutional neural network (CNN) that detects and rejects false alarms to assist analysts with this validation. A CNN is first calibrated with 1,963 DVWF images containing 16,490 detected and AIS associated vessels (referred to as SAR detected and AIS associated (SAR-AIS) vessels) and 5,654 known false alarms mostly off the coasts of Canada. The CNN’s usability is then evaluated, globally, with 94,562 DVWF images containing 209,746 SAR-AIS vessels, 203,559 validated vessels, and 261,499 unknown objects, detected operationally and non-operationally. During calibration, the CNN classified SAR-AIS vessels and false alarms with 91.6% and 96.3% accuracy, respectively. When evaluating usability, the CNN correctly classified 93.7% of the 209,746 SAR-AIS vessels. False alarms were determined for 5.4% of the 203,559 validated vessels, and 58.1% of the 261,499 unknown objects. These results suggest that a CNN designed to detect and reject false alarms could reduce the number of objects requiring validation by approximately 30%.