Analysis and segmentation of remote-sensing images for land-cover mapping

Abstract

Abstract. In the framework of the European Community programme Training and Mobility for Researchers, the project Analysis and Segmentation of Remote-Sensing Images for Land-Cover mapping has been proposed and approved. This article provides some insight in the role of pattern recognition and image processing techniques in the European remote-sensing community and gives and overview of the project's objectives and results to date. Remote-sensing and image analysisRemote Sensing exists by the virtue of physical, economical, political or other constraints that prevent human beings from exploring certain regions. Although many sophisticated instruments have been developed, the hunger for information seems more than ever unsatisfyable, and the more types of information can be extracted from new or existing remote-sensing images, the more economical attractive these data become.It is not our intention to go too much into detail on all the aspects that are related to remote-sensing image analysis. We will limit ourselves to some observations that will help to understand the scientific direction of the project Analysis and Segmentation of Remote-Sensing Images for Land-Cover mapping (ANRS).For an up-to-date, complete and comprehensive textbook on remote-sensing image analysis the reader is referred to [1].Segmentation, classification, and change-detection of remote-sensing images can be formulated as ill-posed problems. Although the quality of many modern sensors is such that these problems are not too ill-posed, this situation changes in cases where one wants to extract types of information for which the sensor has not been build in the first place. This can be the case, for example, when one is interested in the recognition of objects with dimensions close to the pixel resolution.When dealing with complex natural and agricultural landscapes, characterized by the presence of mixed classes and by land-cover/ground responses influenced by terrain topographies, it is not sufficient to base decisions regarding a data element only on the single element itself coming from a single sensor. Regularization of the ill-posed analysis problem is needed such that the uncertainty is reduced sharply, thus improving the consistency of the solution. The information needed to regularize a problem can come, for instance, from