In recent years, addressing spectral variability in hyperspectral data has improved blind hyperspectral unmixing performance and gained attention in endmember detection applications. Current approaches to address the problem of spectral variability associate the variabilities with the valid endmember and attempt to mitigate the ill-effects caused by them. However, intrinsic variabilities induced by material-specific compositional changes are crucial for identifying within-class materials like diverse soil types, forest species, and urban areas. Despite this significance, no studies have attempted a direct implementation to explicitly identify the intrinsic variants of an endmember. In this paper, we propose a framework to solve two important problems: first, to separate the intrinsic variants from illumination-based variants, and second, to simultaneously estimate the number of intrinsic variants and extract their spectral signatures without any knowledge of the number of such sources. The proposed method utilizes a spectral analysis technique with local minima/maxima to remove illumination-based variabilities, followed by a simplex-volume maximization-based reordering of potential endmembers and an iterative reconstruction error-based technique to simultaneously count the number of intrinsic variants and capture their signatures. The approach is validated on synthetic and real datasets, showcasing comparable results with state-of-the-art methods.