In the defence and security domain, camera systems are widely used for surveillance. The major advantage of using camera systems for surveillance is that they provide high‐resolution imagery, which is easy to interpret. However, the use of camera systems and optical imagery has some drawbacks, especially for application in the military domain. In poor lighting conditions, dust or smoke the image quality degrades and, additionally, cameras cannot provide range information too. These drawbacks can be mitigated by exploiting the strengths of radar. Radar performance can be largely maintained during the night, in various weather conditions and in dust and smoke. Moreover, radar provides the distance to detected objects. Since, the strongpoints and weaknesses of radar and camera systems seem complementary, a natural question is: can radar and camera systems learn from each other? Here the potential of radar/video multimodal learning is evaluated for human activity classification. The novelty of this work is the use of radar spectrograms and related video frames for classification with a multimodal neural network. Radar spectrograms and video frames are both two‐dimensional images, but the information they contain is of different nature. This approach was adopted to limit the required preprocessing load, while maintaining the complementary nature of the sensor data.@en

In this paper, a wire detection algorithm is proposed for synthetic aperture radar (SAR) images. The algorithm is specifically designed for SAR images generated from an agile, drone-mounted, omnidirectional radar array to be used for the detection of improvised explosive devices (IEDs). A multistage approach consisting of denoising, constant false alarm rate (CFAR) thresholding, feature extraction, and automated detection using the Radon transform, is proposed and applied to a set of SAR images with multiple aspect angles. At each detection step, the look-angles of individual pixels are used to remove false alarms, and improve detection accuracy. The algorithm is tested using measured data and provides an acceptable detection performance on straight wire segments even in the presence of a strong background clutter. @en

Camera systems are widely used for surveillance in the security and defense domains. The main advantages of camera systems are their high resolution, their ease of use, and the fact that optical imagery is easy to interpret for human operators. However, particularly when considering application in the defense domain, cameras have some disadvantages. In poor lighting conditions, dust or smoke the image quality degrades and, additionally, cameras cannot provide range information. These issues may be alleviated by exploiting the strongpoints of radar. Radar performance is largely preserved during nighttime, in varying weather conditions and in dust and smoke. Furthermore, radar provides range information of detected objects. Since their qualities appear to be complementary, can radar and camera systems learn from each other? In the current study, the potential of radar/video multimodal learning is assessed for the classification of human activity.@en