The Special Case of Sea Mines

In this chapter, work carried out at the Royal Military Academy regarding sea mines and mine countermeasures is summarized. Three sensors used for the detection and identification of sea mines are studied here: sonar, gradiometer and infrared camera. These sensors can be applied to detect different types of sea mines. Some signal and image processing techniques developed to extract relevant information for the detection of underwater objects are presented in this chapter. These techniques are validated using data collected in the frame of different European and NATO projects

An evaluation of pixel-based methods for the detection of floating objects on the sea surface

Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperformthe more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene

Analysis of spatial quality for change detection using SAR images

C

Matched filter based detection of floating mines in IR spacetime

Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging,search-and-rescue and perimeter or harbour defense. IR video was chosen for its day-and-night imaging capability, and its availability on military vessels. Detection is difficult because a rough sea is seen as a dynamic background of moving objects with size order, shape and temperature similar to those of the floating mine. We do find a determinant characteristic in the target's periodic motion, which differs from that of the propagating surface waves composing the background. The classical detection and tracking approaches give bad results when applied to this problem. While background detection algorithms assume a quasi-static background, the sea surface is actually very dynamic, causing this category of algorithms to fail. Kalman or particle filter algorithms on the other hand, which stress temporal coherence, suffer from tracking loss due to occlusions and the great noise level of the image. We propose an innovative approach. This approach uses the periodicity of the objects movement and thus its temporal coherence. The principle is to consider the video data as a spacetime volume similar to a hyperspectral data cube by replacing the spectral axis with a temporal axis. We can then apply algorithms developed for hyperspectral detection problems to the detection of small floating objects. We treat the detection problem using multilinear algebra, designing a number of finite impulse response filters (FIR) maximizing the target response. The algorithm was applied to test footage of practice mines in the infrared

An Evaluation of Pixel-Based Methods for the Detection of Floating Objects on the Sea Surface

<p/> <p>Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperform the more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene.</p

SET-260: A Measurement Campaign for EO/IR Signatures of UAVs: Une campagne de mesure de signature Electro Optique et Infrarouge de Drones dans le cadre du groupe OTAN SET 260

International audienceNATO Research Task Group SET-260 aimed at bringing together experts in EO/IR detection among the NATO community to share detection knowledge and signature data of mini and micro UAVs in an urban environment. Within the program of work of SET-260, a NATO joint trial was organized to collect UAV EO/IR signatures of UAVs in different bands with an urban background. The trial took place in CENZUB, the French armed forces training center for urban combat, in June 2019. In this paper, we present details of this trial and discuss the challenges, pros and cons of detecting UAVs in the different EO/IR bands.Le groupe de travail OTAN sur la recherche SET-260 avait pour objectif de réunir des experts en détection EO / IR. La communauté de l'OTAN partagera ses connaissances en matière de détection et les données de signature des mini et micro drones en milieu urbain. Dans le cadre du programme de travail de SET-260, un essai conjoint de l'OTAN a été organisé pour recueillir les signatures UAV EO / IR des drones dans différents groupes avec un milieu urbain. Le procès a eu lieu à CENZUB, le centre de formation des forces armées françaises pour le combat urbain, en juin 2019. Dans cet article, nous présentons les détails de cet essai et discutons les défis, les avantages et les inconvénients de détection de drones dans les différentes bandes EO / IR