Design and implementation of knowledge base for quantitative remote sensing

Abstract

The remotely sensed images can be understood and analyzed better through comparing the remote sensing spectrum and the spectrum with known land surface structure and physical chemistry parameters. It is unavoidable to compute land surface spectrum based on prior knowledge and remote sensing physical models when we cannot obtain the measured spectrum on suitable time and pixel size. The remote sensing knowledge base integrates data, image, and physical models for quantitative remote sensing. Here in our remote sensing spectral knowledge base, the digital elevation model, land cover and land cover maps and soil map are restored in the spatial database. The climatic changes and spatial patterns of vegetation are restored in the expert system. And component spectrum of vegetation, soil, water, snow, minerals, man-made object are measured and also stored in the database. It is apparent that they themselves have data systems and the systems usually are different with input and output parameter systems of remote sensing physical models. In order to solve the discrepancies, metadata of data and model are put forward. About design of the knowledge base, defining and organizing metadata is key task. Accordingly to data, image and model in the knowledge base, the metadata also consists of metadata about data, metadata about image, and metadata about model. Metadata describes formation, quality and meaning of data, image and model. Data exchanges between database, image base and model base are found on the metadata, and is coordinated by an expert system based on rule, so data extraction and re-organizing will obtain flexible as large as possible. The data-engine extracts data from databases and transfers the data between database and model base, and the model-agent selects suitable models to extend the structure and physical chemistry parameters, and extract land surface facts, finally compute the land surface spectrum on multi-temporal & spatial scales