Abstract
The ortho-rectification of satellite images is normally a time-consuming and expensive task. It can strongly benefit from automatic or semiautomatic procedures in order to avoid field work for ground control survey. This article presents an automated method to automatically georeference satellite images acquired by the ASTER sensor. The method is based on automatic matching of images and Digital Elevation Models (DEM). First a DEM is extracted from the two stereo image bands, which then is matched to the SRTM (Shuttle Radar Topography Mission) DEM in order to correct its position in a map reference system. This allows for the simultaneous correction of image geo-location, which then is followed by the ortho-rectification. The method was applied to an ASTER image from North Portugal and assessed using topographic map data. It was possible to geo-reference images in hilly terrain with positional accuracy better than one pixel.

Abstract
There is great interest in the development of automatic geometric correction systems for satellite images. A fully automatic system, based exclusively on the identification of tie points (image to image control points) by image matching needs to use efficient selection and validation methods. Four Tie Point Suitability Indices (TPSI) are proposed to select the most suitable areas in an image to search for tie points. Three tie point validation parameters are also proposed. The validation parameters make use of the various spectral bands available in hyperspectral and multispectral satellite images. The proposed TPSIs and validation parameters were tested with hyperspectral high-resolution satellite images from the CHRIS/PROBA sensor.

Abstract
Here we present a motion tracking algorithm and a fast river detection algorithm using a near infrared camera on-board an UAV. These algorithms have to run on a slow PC104. The motion tracking algorithm is based on the Normalized Cross-Correlation and the river detection algorithm uses Meyer's Watershed method. Simulation results with a video are presented.

Abstract
The aim of this study is the application of ERS data (ATSR2 and radar altimetry) to fisheries monitoring in the Cape Verde region. This work is being done in the scope of the ESA A03-265 project. The Cape Verde Archipelago lies under the influence of the southern portion of the Canarias Current, where it turns westward and becomes the North Equatorial Current. Cold and nutrient-rich waters upwelled at the Western African coast are transported south-westward along this current system. However, due to the seasonal north-south migration of the current system, the south-eastern region of the islands can be affected by the Equatorial Countercurrent. Two very different water masses meet forming a large scale frontal system in the vicinity of the archipelago. These frontal zones are potentially favourable for the aggregation of large pelagic highly migratory fish species. The knowledge of the front location and variability has a great socio-economic importance for the local fishing industry, as well as in the support of global stock management of these species. Using maps of sea surface topography and sea level anomalies derived from satellite altimetry and maps of sea surface temperature from ATSR2, the general features of the ocean circulation in the study area have been investigated. Due to the fact that Cape Verde region has very cloudy conditions, the synergy of microwave and infra-red data is of great importance for this study. The methodology used in this analysis and the results obtained so far are presented. Future work involves the use of in situ data to validate satellite derived information and the investigation of the relationships between fishing success and the ocean conditions measured by satellite and in situ methods. The ultimate goal will be the development of an operational methodology for the location of potential favourable areas for the concentration of large pelagic fish in the area of local fishing fleet activity.

Abstract
A system for near real time processing of NOAA-AVHRR satellite data is described. The system allows for the final imagery, geometrically corrected and calibrated, to be obtained only a few minutes after the satellite pass. An application example is presented: the production of snow cover weekly composites for Scotland during the winter of 1996.

Abstract
A fully automatic methodology based on image processing is proposed to evaluate the quality of spray application sampled by water-sensitive papers (WSP). The methods proposed permit a computation of the fraction of spray coverage, an evaluation of the homogeneity of the spray spatial spread at various scales and directions, and extraction of stain and droplet size range and distribution. This allows the number of droplets per unit area and the standard droplet size spectra factors to be computed. The methods were tested with a number of test samples scanned at different resolutions, proving to be effective in situations where there is high spray coverage in the WSP, thus with considerable overlap between stains. The most suitable scanning resolution was found to be 600 dpi. The results obtained by the image processing methods were successfully compared with a manual (visual) counting of stains in a test sample.