Semantic Remote Sensing Scenes Interpretation and Change Interpretation - Archive ouverte HAL
Chapitre D'ouvrage Année : 2018

Semantic Remote Sensing Scenes Interpretation and Change Interpretation

Résumé

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.

Dates et versions

hal-01956201 , version 1 (15-12-2018)

Identifiants

Citer

Fethi Ghazouani, Imed Riadh Farah, Basel Solaiman. Semantic Remote Sensing Scenes Interpretation and Change Interpretation. Ontology in Information Science, Intech, pp.205 - 224, 2018, ⟨10.5772/intechopen.72730⟩. ⟨hal-01956201⟩
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