Remote sensing (RS) image processing can be converted to an optimization problem, which can then be solved by swarm intelligence algorithms, such as the artificial bee colony (ABC) algorithm, to improve the accuracy of the results. However, such optimization algorithms often result in a heavy computational burden. To realize the intrinsic parallel computing ability of ABC to address the computational challenges of RS optimization, an improved multiagent (MA)-based ABC framework with a reduced communication cost among agents is proposed by utilizing MA technology. Two types of agents, massive bee agents and one administration agent, located in multiple computing nodes are designed. Based on the communication and cooperation among agents, RS optimization computing is realized in a distributed and concurrent manner. Using hyperspectral RS clustering and endmember extraction as case studies, experimental results indicate that the proposed MA-based ABC approach can effectively improve the computing efficiency while maintaining optimization accuracy.