Feature labelling in infrared oceanographic images

Abstract

Thermal infrared images of the ocean obtained from satellite sensors are widely used for the study of ocean dynamics. The derivation of mesoscale ocean information from satellite data depends to a large extent on the correct interpretation of infrared oceanographie images. The difficulty of the image analysis and understanding problem for oceanographic images is due, in large part, to the lack of precise mathematical descriptions of the ocean features, coupled with the time varying nature of these features and the complication that the view of the ocean surface is typically obscured by clouds, sometimes almost completely. Towards this objective a technique that utilizes a non-linear probabilistic relaxation method for the oceanographie feature labelling problem is described. A unified mathematical framework that helps in solving the problem is presented, and the advantages of using the contextual information in the feature labelling algorithm is highlighted. The feature labelling technique makes use of a new, efficient edge detection algorithm based on cluster shade texture measures. This algorithm is found to be more suitable for labelling the mesoscale features present in the oceanographie satellite images. Some important results of a series of experiments conducted at NOARL's Remote Sensing Branch on NOAA AVHRR imagery data are presented. A motivation for using this technique to build an automatic image interpretation system is also given.