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Digital Elevation models (DEM) are images having terrain information embeddedinto them. Using cognitive mapping concepts for DEM registration, has evolvedfrom this basic idea of using the mapping between the space to objects anddefining their relationships to form the basic landmarks that need to bemarked, stored and manipulated in and about the environment or other candidateenvironments, namely, in our case, the DEMs. The progressive two-levelencapsulation of methods of geo-spatial cognition includes landmark knowledgeand layout knowledge and can be useful for DEM registration. Space-basedapproach, that emphasizes on explicit extent of the environment underconsideration, and object-based approach, that emphasizes on the relationshipsbetween objects in the local environment being the two paradigms of cognitivemapping can be methodically integrated in this three-architecture for DEMregistration. Initially, P-model based segmentation is performed followed bylandmark formation for contextual mapping that uses contextual pyramidformation. Apart from landmarks being used for registration key-point finding,Euclidean distance based deformation calculation has been used fortransformation and change detection. Landmarks have been categorized to belongto either being flat-plain areas without much variation in the land heights;peaks that can be found when there is gradual increase in height as compared tothe flat areas; valleys, marked with gradual decrease in the height seen inDEM; and finally, ripple areas with very shallow crests and nadirs. Fractalbased compression was used for storage of co-registered DEMs. This method mayfurther be extended for DEM-topographic map and DEM-to-remote sensed imageregistration. Experimental results further cement the fact that DEMregistration may be effectively done using the proposed method.

Cognitive-mapping and contextual pyramid based Digital Elevation Model Registration and its effective storage using fractal based compression