Because of the improvement of optical remote sensing instrument, hyperspectral images now collect information of the ground with hundreds of wavelengths. This spectral information can be used to identify different materials, since each material should have its unique absorption spectrum. Traditionally the spectra was discriminated by measuring either the spectral distance or angle between two spectra directly. However, the remote spectra usually contain noises and interferences from other sources, and even the same material has various spectra. In this case, the conventional measurements may not have the capability enough to tolerate the distortions and to identify each material. In this study, the Ensemble Empirical Mode Decomposition (EEMD) is adopted to measure the similarity between the spectra and discriminate materials. EEMD not only can decompose the spectrum into several components as original Empirical Mode Decomposition (EMD), but also compensating the noises and interferences in the signal as an improved version. Although EEMD is a time-consuming process, its structure is suitable for parallel computing. In this paper we propose a graphic-processing-unit (GPU)-based EEMD on a cluster. Experimental results showed that it can extract the spectral features more effectively than common spectral similarity measures, and it has better ability in characterizing spectral properties. It also demonstrated that the proposed GPU-based high-throughput EEMD achieved a significant 60.62x speedup compared to its CPU-based single-threaded counterpart written in C language.