As the volume of hyperspectral data utilized in planetary exploration continues to grow, the development of efficient and accurate algorithms for data analysis becomes increasingly critical. This study explores the potential of spectral unmixing techniques to analyze hyperspectral images of Mars. Observations from the compact reconnaissance imaging spectrometer for mars (CRISM) serve as the primary dataset. Preprocessing steps, including atmospheric correction and mitigation of the “Smile” spectral effect, are performed to minimize noise and provide a robust foundation for subsequent spectral profile analysis. The number of endmembers in hyperspectral images of the Jezero impact crater is estimated using an eigenvalue-based method. Specifically, the eigenvalue maximum likelihood method is employed to define a likelihood function that determines the optimal number of endmembers by identifying the global maximum without the need for threshold adjustments. This approach achieves reliable results even under low signal-to-noise ratio conditions. Subsequently, the vertex component analysis (VCA) algorithm is applied to decompose and extract the mixed endmembers in the images. The extracted results are compared with the CRISM spectral library, and key absorption features in the spectral curves are analyzed to identify specific minerals. This methodology enables precise identification of mineral components within the Jezero impact crater, including water-bearing silicate and carbonate minerals. These findings suggest that Mars may have once sustained a liquid water environment conducive to life and experienced a warmer, wetter climate during its ancient history.