Semantics and High Performance Computing Driven Approaches for Enhanced Exploitation of Earth Observation (EO) Data: State of the Art

Abstract

This review paper discusses the state of the art in earth observation image (EO) information mining from a semantics based approaches. The need for mining of RS data has arisen due to the ever increasing amount of data that is being collected by various EO platforms. The EO data archives are reaching to unmanageable sizes, and the challenges in storing and disseminating this information is reaching alarming proportions. It is believed that 90% of this data remains in the archives, untouched. This is due to various reasons, such as the inability to search and find the data, the lack of contextualization in the searching and retrieving of the relevant data, the humongous amount of time required to process these datasets, etc. Currently, the data is made available to the user through interfaces that support only syntactical queries, and lack the intuitiveness which does not cater to the user’s conjecture. These limiting factors highly affect the usage of these archived datasets. This review which is based on selected papers covers two areas of earth observations, which can benefit from the integration of semantic technologies: (1) data from EO imagery (2) EO data in the form of thematic data. Further, to work on huge EO image databases the computational power needs to be increased exponentially. The recent advent of graphical processing units (GPU’s) for general purpose computing has tremendously helped in developing rapid approaches for the mining EO image archives. The various processes involved in using GPU computing in a variety of EO applications is discussed along with the recent work in this domain.