An implementation of a simplified load balancing scheme was investigated to provide a paralleling way of processing Unmanned Aerial Vehicle (UAV) imagery based on common computing and network resources for urgent response. A load balancing strategy, which actively monitors computing resources at the process-level and dynamically manages two-stage mission queues, was adopted. Building a lite parallel processing system under urgent circumstances using this method will provide a flexible, efficient, and robust way to accelerate UAV imagery production markedly, based on available computing resources. Orthorectification and mosaicing of UAV imagery using position and orientation parameters provided by onboard positioning and orientation systems using digital elevation data was carried out to validate the effectiveness of this method. Results show that the proposed solution is easy to implement, convenient to deploy, flexible, and extends and rationalizes mission allocation, leading to an strong correlations between computing nodes and efficiency, thus improving orthophotomap productivity when using UAV imagery under urgent circumstances.