Extending an open source spatial Database with geospatial image support: An image mining perspective

The nature of vector data is relatively constant, and it is revised less frequently as compared to remotely sensed earth observation data. Remote sensing images are being collected nowadays every 15 minutes from satellites such as Meteosat. In the coming years, very high spatial resolution data is expected to be available freely and frequently. Integrated GIS and remote sensing spatial analysis methods have the ability to incorporate different data sources to find attribute associations and patterns of change for knowledge discovery and change detection. GIS-based data such as vector data and DEM are overlayed with image data and results are taken up in a GIS for further processing and analysis. A platform is required to efficiently store, retrieve and manipulate such image data as layers just like other GIS data layers for hybrid GIS/RS analysis. In principle, spatial databases are the most suitable candidates as such a platform. Our work is aimed to investigate the open source spatial database PostgreSQL/PostGIS (PG/PG) as such a platform, to provide a solution for image support and an overall framework for integrated remote sensing and GIS analysis. This is definitely beyond just storage and retrieval of images in spatial databases. The requirements and available open source libraries were extensively studied to provide such an image support. The TerraLib library was proposed, and analysed to extend the PG database with image support. To demonstrate the application developed in this study, the Meteosat Second Generation (MSG) image data for a larger part of Europe was extracted from the ITC data receiver. An application programme was written to construct time series image database for extracted image data with the PG/ PG DBMS. A mining application to detect clouds patterns from time-series image and vector data stored in the PG database was developed using TerraLib conceptual schema. For this, an extensive study of data mining methods was carried out. A statistical data mining method based on the principal components analysis was adopted to extract cloud features for the Netherlands from the time series image data. Using this research platform and cloud patterns detection case application, various image mining scenarios were conducted to provide a framework for integrated image and vector data analysis top of the DBMS technology. This framework is extremely useful for studying spatio-temporal phenomena with seasonal or long intervals and region-based studies where the regions on a remote sensing image are extracted by vector data.

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