International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Data Mining and Knowledge Innovation Tools for Managing Big Earth Observation Images

Author : B. Arunamma ¹ P.Balaji ²

Date of Publication :7th August 2016

Abstract: The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge about the patterns hidden in the images, to retrieve dynamically a collection of relevant images, and to support the creation of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In this context, deals with knowledge discovery from Earth-Observation (EO) images, related geospatial data sources and their associated metadata, mapping the extracted low-level data descriptors into semantic classes and symbolic representations, and providing an interactive method for efficient image information mining. with providing a platform for data mining and knowledge discovery content from EO archives. The platform’s goal is to implement a communication channel between Payload Ground Segments and the end-user who receives the content of the data coded in an understandable format associated with semantics that is ready for immediate exploitation. It focuses on the design and implementation of methods for the extraction of relevant descriptors (features) of EO images, specifically Terra SAR-X images, physical integration (fusion) and combined usage of raster images and vector data in synergy with existing metadata. It will provide the user with automated tools to explore and understand the content of highly complex images archives. The challenge lies in the extraction of meaningful information and understanding observations of large extended areas, over long periods of time, with a broad variety of EO imaging sensors in synergy with other related measurements and data.

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