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Sharing spatial data derived from remote sensing is a very significant thing. Grid computing and Web Service technology provides fundamental support for it. In this paper we mainly discuss architecture and middleware of sharing spatial data derived from remote sensing and processing. Because middleware of automatically transferring and task execution on grid is the key of the architecture, we study the middleware. It can effectively protect the owner of data and middleware's property through giving users their required result not just simply copying data and codes resource to them. Based on this sharing architecture and middleware technology, a data and middleware transferring example is showed. [ABSTRACT FROM AUTHOR]

For many remote sensing application projects, the quality of the research or the product is heavily dependent upon the quantity of computing cycles available. Middleware is software that connects two or more otherwise separate applications across the Internet or local area networks. In this paper, we present the High Throughput Computing Spatial Information Processing (HIT-SIP) System (Prototype), which is developed in Institute of Remote Sensing Applications, Chinese Academy of Sciences, China. Several middleware packages developed in the HIT-SIP system are demonstrated. Our experience shows that it is feasible that our grid computing testbed can be used to do remote sensing information analysis. [ABSTRACT FROM AUTHOR]

Remote sensing applications often concern very large volumes of spatio-temporal data, the emerging Grid computing technologies bring an effective solution to this problem. The Open Grid Services Architecture (OGSA) treats Grid as the aggregate of Grid service, which is extension of Web Service. It defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. It is not effective used in data-intensive computing such as remote sensing applications because its foundation, Web Service, is not efficient in scientific computing. How to increase the efficiency of the grid services for a scientific computing? This paper proposes a mechanism Grid service spread (GSS), which dynamically replant a Grid service from a Grid node to the others. We have more computers to provide the same function, so less time can be spent completing a problem than original Grid system. This paper also provides the solution how to adept the service duplicate for the destination node's Grid environment; how each service duplicate communicates with each other; how to manage the lifecycle of services spread etc. The efficiency of this solution through a remote sensing application of NDVI computing is demonstrated. It shows that this method is more efficient for processing huge amount of remotely sensed data. [ABSTRACT FROM AUTHOR]