The development of efficient techniques for transforming the massive volume of remotely sensed hyperspectral data collected on a daily basis into scientific understanding is critical for space-based Earth science and planetary exploration. Although most available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of computers represent a promising cost-effective solution expected to play a major role in many on-going and planned remote sensing missions. To address the need for cost-effective parallel hyperspectral imaging algorithms, this paper develops an innovative heterogeneous parallel algorithm for spatial/spectral morphological analysis of hyperspectral image data. The algorithm has been developed using Heterogeneous MPI (HeteroMPI), an extension of MPI for programming high-performance computations on heterogeneous networks of computers. Experimental results are presented and discussed in the context of a realistic application, based on hyperspectral data collected by NASA?s Jet Propulsion Laboratory.