An extension of a newly developed cluster-space representation is applied to efficient data transmission and classification. Cluster-space classification, which is an automatic hybrid supervised and unsupervised classification procedure, can be performed in two stages. A "semiproduct" with low entropy is generated at the sender end. It is then transmitted to a range of users for further classification. Experiments using a HyMap dataset demonstrate the advantages in data transmission and the satisfactory classification accuracy.