EXTRACTING AN IMAGE SIMILARITY INDEX USING META¬DATA CONTENT FOR IMAGE MINING APPLICATIONS

摘要

The motivation behind this research has been to identify, and where possible, minimize or eliminate potential problem areas facing NASA in its mission of gathering and analyzing remotely-sensed imagery in both Earth and space disciplines. Managing and extracting useful information from the massive image databases resulting from such missions is a challenging task for NASA. The key to real-time archival and retrieval of this massive image data lies in the notion of content based image data management. Two major steps are involved in this process. The first one is to automatically extract image content or meta-data from satellite imagery. The second one is to organize this database to permit users from numerous disciplines and communities to access data relevant to their needs. Accordingly, each data set is indexed in multiple ways, enabling users to retrieve images by specifying constraints over a combination of attributes. One such method provides users the ability to search the data holdings using a metric of similarity in content so that neighboring images in the database have a high probability of hit when queried for a specific type of meta-data content. An important area of research is therefore to compute and evaluate similarity measures for images. In this paper, we present a backpropagation neural network based technique to classify a multispectral satellite image and extract a similarity measure using the meta-data classification content.