Distributed sensor networks empower real-time in-situ computing and seismic imaging without transmitting the raw data to the remote data center. This attribute is valuable for planet exploration that has stringent bandwidth. The previously distributed ambient noise imaging algorithm computes results using data from near neighbors, while the information from distant neighbors is not utilized, hence the image quality is compromised. To overcome this problem, this work proposes an innovative common-receiver-based decentralized ambient noise imaging algorithm. In this algorithm, the basic imaging algorithm is still Eikonal tomography, but the distant neighbors are also used to computing the seismic image so that the quality of the output image can be preserved. An in-situ computing and clustering algorithm is created to optimize the data transition and computation while meeting the bandwidth constrains. The experiments were performed on both synthetic data from Enceladus and real data from the USArray archives. The new algorithm generates higher-resolution images under the same bandwidth constraints, comparing to previous algorithms, and the quality of the output image is satisfactorily preserved. The communication cost reduction over the raw data collection is in several orders of magnitude (e.g., 1: 1600). It meets the desired bandwidth constraint in planetary exploration applications.