A KML-BASED APPROACH FOR DISTRIBUTED COLLABORATIVE INTERPRETATION OF REMOTE SENSING IMAGES IN THE GEO-BROWSER
Keywords: Image Interpretation, Distributed Collaboration, Collaborative interpretation, KML, GeoGlobe
Abstract. Existing implementations of collaborative image interpretation have many limitations for very large satellite imageries, such as inefficient browsing, slow transmission, etc. This article presents a KML-based approach to support distributed, real-time, synchronous collaborative interpretation for remote sensing images in the geo-browser. As an OGC standard, KML (Keyhole Markup Language) has the advantage of organizing various types of geospatial data (including image, annotation, geometry, etc.) in the geo-browser. Existing KML elements can be used to describe simple interpretation results indicated by vector symbols. To enlarge its application, this article expands KML elements to describe some complex image processing operations, including band combination, grey transformation, geometric correction, etc. Improved KML is employed to describe and share interpretation operations and results among interpreters. Further, this article develops some collaboration related services that are collaboration launch service, perceiving service and communication service. The launch service creates a collaborative interpretation task and provides a unified interface for all participants. The perceiving service supports interpreters to share collaboration awareness. Communication service provides interpreters with written words communication. Finally, the GeoGlobe geo-browser (an extensible and flexible geospatial platform developed in LIESMARS) is selected to perform experiments of collaborative image interpretation. The geo-browser, which manage and visualize massive geospatial information, can provide distributed users with quick browsing and transmission. Meanwhile in the geo-browser, GIS data (for example DEM, DTM, thematic map and etc.) can be integrated to assist in improving accuracy of interpretation. Results show that the proposed method is available to support distributed collaborative interpretation of remote sensing image
