The problem of detecting airborne targets in a sequence of images recorded by a long range infra-red sensor is investigated. The generalised likelihood ratio test detector is derived. The detector structure and its actual implementation are discussed. Approximated expressions of the false alarm and detection probabilities are obtained and validated by means of simulations. Example results obtained on a set of experimental data are reported.

Navy infrared search and track (IRST) systems aim at detecting long range airborne targets closing the naval unit at low altitude above the sea surface.1 The target signal is a 2-D pulse of small spatial extent and is well approximated by the sensor point spread function (PSF) further, the signal to clutter ratio (SCR) is usually very low and detection cannot be performed on a single frame. Multiple frame detection algorithms are therefore used to improve the detection probability.

The clutter removal procedure for infrared (IR) naval surveillance systems presented is designed to manage a typical maritime scenario and is insensitive to the sharp transition between sea and sky across the horizon line. It is also effective for the removal of striping noise which arises as a consequence of the nonuniform calibration of the detector array. The low computational cost of this technique makes it well suited for real-time implementation. The effectiveness of the clutter removal procedure is illustrated on a set of experimental IR data.

This paper investigates the problem of detecting airborne targets in a sequence of images recorded by a long range InfraRed (IR) sensor. The target appears in the IR images as a small, weak signal embedded in a strong background clutter. It is assumed that the target's amplitude, velocity and position are unknown parameters. To accommodate the unknown parameters the Generalized Likelihood Ratio Test (GLRT) detector is derived. The detector structure and its actual implementation are discussed in detail. To test the detection algorithm an experiment involving a cooperating aircraft has been performed. The preliminary results obtained on this set of experimental data are presented and discussed.

This paper presents the results of a combined empirical orthogonal function (EOF) analysis of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) data and sea-viewing wide field-of-view sensor (SeaWiFS) chlorophyll concentration data over the Alboran Sea (Western Mediterranean), covering a period of 1 year (November 1997–October 1998). The aim of this study is to go beyond the limited temporal extent of available in situ measurements by inferring the temporal and spatial variability of the Alboran Gyre system from long temporal series of satellite observations, in order to gain insight on the interactions between the circulation and the biological activity in the system. In this context, EOF decomposition permits concise and synoptic representation of the effects of physical and biological phenomena traced by SST and chlorophyll concentration. Thus, it is possible to focus the analysis on the most significant phenomena and to understand better the complex interactions between physics and biology at the mesoscale. The results of the EOF analysis of AVHRR-SST and SeaWiFS-chlorophyll concentration data are presented and discussed in detail. These improve and complement the knowledge acquired during the in situ observational campaigns of the MAST-III Observations and Modelling of Eddy scale Geostrophic and Ageostrophic motion (OMEGA) Project.

In this paper we investigate on a ranging system based on the images recorded by a single passive camera. To estimate the distance from an object it is necessary to know its dimensions, so the observed target must be identified. The target is assumed to be a translated, rotated and scaled version of the corresponding reference image included in a database. The distance to the object is estimated by comparing the extracted target with its reference image taken at a known distance. We consider two different Automatic Target Recognition (ATR) techniques and compare their performances on a set of simulated data. Namely we evaluate the probability of correct recognition and the relative error in distance estimation. Finally, we discuss the results obtained by processing an experimental sequence of images recorded by an IR camera.

By means of time series analysis of sea surface temperature (SST) maps it is possible to investigate the temporal and spatial variability of surface currents: such investigation is the basis for the comprehension of the relationship existing between 3D circulation and surface structures. In this work the study of SST variability is focused on the Alboran Sea, Western Mediterranean. The Alboran Sea communicates with the Atlantic Ocean through the Strait of Gibraltar and is bounded on the north by Spain and on the south by Morocco and Algeria. The main oceanographic features of the area are constituted by: 1) the intense baroclinic jet associated to the Atlantic Water stream, forming one or two anti-cyclonic gyres (the westernmost of which is the more persistent and is usually called Western Alboran Gyre, WAG); 2) a strong density and temperature front between Spain and Africa (the Almeria-Oran Front, AOF); 3) mesoscale phenomena associated to the jet and the frontal areas. The MAST-III OMEGA Project (Observing and Modelling of Eddy scale Geostrophic and Ageostrophic circulation) aims at better understanding of this mesoscale system, with particular concern to vertical velocity and its effects on biology. Both remotely sensed data obtained from TM and AVHRR satellite sensors and in-situ measurements were used; the latter were gathered during two cruises: the BIO Hesperides cruise, carried out in the WAG area in October 1996, and the RSS Discovery cruise, carried out in the AOF area in December 1996. The empirical orthogonal function decomposition (EOF) is the method the authors use to analyse SST time series. EOF consists of representing each image of the original sequence on a new basis whose functions are intrinsic to the particular data set because wholly determined by the dataset itself, hence the designation empirical.