Remotely Sensed Image Processing Service Composition Based on Heuristic Search

As remote sensing technology become ever more powerful with multi-platform and multi-sensor, it has been widely recognized for contributing to geospatial information efforts. Because the remotely sensed image processing demands large-scale, collaborative processing and massive storage capabilities to satisfy the increasing demands of various applications, the effect and efficiency of the remotely sensed image processing is far from the user's expectation. The emergence of Service Oriented Architecture (SOA) may make this challenge manageable. It encapsulate all processing function into services and recombine them with service chain. The service composition on demand has become a hot topic. Aiming at the success rate, quality and efficiency of processing service composition for remote sensing application, a remote sensed image processing service composition method is proposed in this paper. It composes services for a user requirement through two steps: 1) dynamically constructs a complete service dependency graph for user requirement on-line; 2) AO* based heuristic searches for optimal valid path in service dependency graph. These services within the service dependency graph are considered relevant to the specific request, instead of overall registered services. The second step, heuristic search is a promising approach for automated planning. Starting with the initial state, AO* uses a heuristic function to select states until the user requirement is reached. Experimental results show that this method has a good performance even the repository has a large number of processing services.