## 1 Introduction  
Measurements over the oceans have been used extensively for weather forecasting, ship safety and global-scale studies of climate and sea conditions. The marine coastal environment is characterized by the interaction of a complex set of upper ocean and atmospheric boundary layer processes having spatial and temporal scales ranging from meters to hundred of kilometers and from seconds to several days. Mesoscale processes such as upwellings, eddies, thermal fronts or jet currents are very energetic and their knowledge is very important, not only to study the oceanic circulation, but also in other applications that include acoustic propagation anomalies, fisheries management and exploitation, coastal monitoring and offshore or ocean oil detection and exploitation. Infrared (IR) and visible (VIS) images soon came to play a major contribution in oceanographic observation, in particular, to study the ocean dynamics. However, all those studies involve analyzing large data sets of IR and VIS imagery. So, it is desirable to replace the labor-intensive time-consuming manual interpretation with automated analysis tools. So, in many remote sensing applications using satellite images and specifically in those related to oceanographic studies, it is necessary to compare multiple images of the same scene acquired by different sensors, or images taken by the same sensor but at different time instants. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensorial data fusion, change detection, integrating information into geographic information systems (GIS) and environment monitoring. Such a comparison of multiple images requires either their georeferencing or their spatial registration. Several techniques for the georeferencing of images from the same area have been proposed [1-2]. However, in the framework of oceanographic studies, the comparison of multi-temporal and multisensorial images is performed by the spatial registration. Image registration is the process that determines the best spatial fit between two or more images (reference and sensed images) that overlap the same physical region of the scene being imaged, acquired at the same or at difference date, by identical or difference sensors. In general, its applications can be divided into three main groups according to the manner of the image acquisition [3-5]:  
\(\bullet\) Different times (multitemporal analysis). Images of the same scene are acquired at different times, often on regular basis, and possibly under different conditions. The aim is to find and evaluate changes in the scene which appeared between the consecutive images acquisitions, _i.e._, tracking of specific oceanographic structures [6]. \(\bullet\) Different sensors (multimodal analysis). Images of the same scene are acquired by different sensors or by the same sensor at different resolutions. The aim is to integrate the information obtained from different source streams to gain more complex and detailed scene representation as described by [7,8] (_i.e._, fusion of information from sensors with different characteristics like panchromatic images, offering better spatial resolution, color/multispectral images with better spectral resolution, or radar images independent of cloud cover and solar illumination). \(\bullet\) Scene to model registration. Images of a scene and a model of the scene are registered. The model can be a computer representation of the scene, for instance, maps or digital elevation models in GIS. The aim is to localize the acquired image in the scene/model and/or to compare them. (_i.e._, registration of satellite data into maps or other GIS layers). Due to the diversity of images to be registered and due to various types of degradations, it is impossible to design a universal method applicable to all registration tasks. To solve image registration problem for various types of data, over the years, several methods and techniques have been proposed in the scientific literature [5,9].