ARIOS 2 - Assisted Land Cover Recognition and Interpretation (phase 2)

Context and objectives

This research project aims to improve techniques of remote sensing image processing for land cover mapping and their integration into a geographic information system. The quality and the updating of these inventories are to be the basis of a sustainable and efficient land management.
The purpose is to develop a method improving the recognition and the interpretation of land cover, in order to improve the use of spatial information and ancillary data during the interpretation of multi-sensors and multi-sources data, to preserve classification rules, and to stock the expertise resulting from the interpretation in order to use it again (updating or more detailed inventory on a larger scale).

Two kinds of numerical classification are developed :

Firstly, classifications by pixel are computed (see ARIOS – phase 1 - T4/DD/007). The aim was to show the importance of the integration of textural and contextual information during the classification. In the same way, the artefacts created by the use of this kind of spatial information are suppressed thanks to the use of a multiple classifier. In spite of the introduction of the texture and the context as well as the use of a complex classifier, the « salt-and-pepper » effect persists. Some post - classification filters will be used to generalise the picture, to increase the level of abstraction of this classification and so to come closer of the visual interpretation CORINE Land Cover.

During this second phase, the accuracy of the classification were improved with the use of objects. The images are segmented in regions while using techniques of borders detection. New features of texture and context were computed.
When regions are obtained and the selected discriminative features computed, the objects are classified on basis of different kind of classifiers. The degree of abstraction and the level of generalisation will come closer of the interpretation visual.