Multisource Aggregation Search and Scheduling for Remote Sensing Data Cluster

Multisource aggregation (MSA) is an important function in the remote sensing data processing. In this letter, we propose and study a novel MSA search and scheduling problem to improve the performance of remote sensing data cluster. Given a static spatial network, a set of query points Q, and a set of data locations O (candidates for cluster centers), the MSA function retrieves the data location with the minimum aggregation distance (the sum of the distances to all query points). We believe that such function plays an important role in remote sensing data cluster and classification. The MSA problem faces two challenges: 1) how to prune the search space effectively and retrieve the results of MSA in real time and 2) how to schedule multiple query sources during search processing. To overcome these challenges, we make the following contributions. First, upper and lower bounds on aggregation distance are defined to prune the search space effectively. Second, each query source is given a priority label, and a best-first scheduling strategy is developed to further enhance the query performance. Finally, we conduct extensive experiments on real and synthetic data sets to verify the high performance of the developed algorithms.