Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 627-633, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-627-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
29 Apr 2015
Data Mining and Knowledge Discovery tools for exploiting big Earth-Observation data
D. Espinoza Molina and M Datcu German Aerospace Center (DLR), Earth Observation Center, Remote Sensing Institute, Oberpfaffenhofen, 82234 Weßling, Germany
Keywords: Systems to manage Earth-Observation images, data mining, knowledge discovery, query engines 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, we are concerned 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 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. The platform is composed of several components such as 1.) ingestion of EO images and related data providing basic features for image analysis, 2.) query engine based on metadata, semantics and image content, 3.) data mining and knowledge discovery tools for supporting the interpretation and understanding of image content, 4.) semantic definition of the image content via machine learning methods. All these components are integrated and supported by a relational database management system, ensuring the integrity and consistency of Terabytes of Earth Observation data.
Conference paper (PDF, 2410 KB)


Citation: Espinoza Molina, D. and Datcu, M.: Data Mining and Knowledge Discovery tools for exploiting big Earth-Observation data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 627-633, https://doi.org/10.5194/isprsarchives-XL-7-W3-627-2015, 2015.

BibTeX EndNote Reference Manager XML