Abstract
A fundamental objective of remote sensing imagery is to spread out the knowledge about our environment and to facilitate the interpretation of different phenomena affecting the Earth's surface. The main goal of this chapter is to understand and interpret possible changes in order to define subsequently strategies and adequate decision-making for a better soil management and protection. Consequently, the semantic interpretation of remote sensing data, which consists of extracting useful information from image date for attaching semantics to the observed phenomenon, allows easy understanding and interpretation of such occurring changes. However, performing change interpretation task is not only based on the perceptual information derived from data but also based on additional knowledge sources such as a prior and contextual. This knowledge needs to be encoded in an appropriate way for being used as a guide in the interpretation process. On the other hand, interpretation may take place at several levels of complexity from the simple recognition of objects on the analyzed scene to the inference of site conditions and to change interpretation. This chapter presents semantic scenes analysis for change interpretation strategy in remote sensing imagery. The presented strategy is composed of different levels of interpretation. For each level, information elements such as data, information, and knowledge need to be represented and characterized. This chapter highlights the importance of ontologies exploiting for encoding the domain knowledge and for using it as a guide in the semantic scene interpretation task.
Highlights
A fundamental objective of remote sensing is to spread out the knowledge about our environment and to facilitate the interpretation of different phenomena affecting the Earth’s surface
An information element, is “an entity composed of a definition set and a content set linked by a functional relationship called informative relation, associated with internal and external context”
Spatial relationships include neighborhood relations (such as externally connected (EC), disconnected (DC), and non-tangential proper part (NTPP)); directional relations describing relative orientations of objects (e.g. North and South); and distance relations. All these spatial relations are formulated in a spatial relation ontology as presented in [26], and they are integrated as parts of an external context in the structure of information element
Summary
A fundamental objective of remote sensing is to spread out the knowledge about our environment and to facilitate the interpretation of different phenomena affecting the Earth’s surface. The inclusion of this contextual information allows to understand the semantics of objects [9] Up to now, both change detection approaches (i.e. pixel-based and object-based methods) have been successful either for detecting simple binary change/non-change (i.e. answering “are there changes”?) or for detailing “from-to” change between different classes (i.e. what change?). Highlighting the role of the remote sensing imagery for change detection and interpretation, an appropriate semantic interpretation method is needed for change interpretation in satellite images Such methods should take into account the description and the representation of different information elements at each interpretation level. The objectives of this chapter are to describe the semantic scene interpretation strategy including the definition and the representation of different information elements composing that process
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