Hyperspectral imaging provides a powerful means of material identification by capturing detailed spectral information across a broad range of wavelengths. However, the accurate classification of materials remains challenging due to spectral mixing and the lack of ground truth data. This paper proposes a robust approach for hyperspectral endmember extraction and material identification by integrating the N-FINDR algorithm with spectral information divergence (SID)-based spectral library matching. The proposed method enhances segmentation accuracy by identifying pure spectral signatures (endmembers) and associating them with actual materials using the ECOSTRESS spectral library. Experimental validation using the Pavia University hyperspectral dataset demonstrates the effectiveness of this approach in extracting, identifying, and segmenting endmember materials with high precision. The results underscore the advantages of combining geometric-based endmember extraction with spectral matching techniques to improve hyperspectral image analysis.