In the hyperspectral unmixing literature, endmember extraction is addressed majorly using three methods i.e. Statistical, Sparse-regression and Geometrical. The majority of the endmember extraction algorithms are developed based on only one of the methods. Recently, GSEE (Geo-Stat Endmember Extraction) has been proposed that combines the geometrical and statistical features. In this paper, we propose a Modified GSEE (MGSEE) algorithm which considers the removal of noisy bands. In the proposed work, the Minimum Noise Fraction (MNF) is used to select high SNR bands. The strength of the MGSEE framework is scrutinized using a synthetic and real benchmark dataset. In this paper, we show that the proposed algorithm obtained from the GSEE by preceding the noise removal step greatly decreases Spectral Angle Error (SAE) and Spectral Information Divergence (SID) error thus indicating its importance to extract pure material in the unmixing problem.

[Planetary Analytics Network for Geo-Environmental Assessment] Research-grade AI diagnostics framework leveraging satellite, ground, and OSINT data to analyze urban-environmental dynamics, benchmark sustainability, and recommend policy interventions based on historical analogues.

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Open, auditable methodology for evaluating AI search optimization (GEO/AEO) tools. 51 scoring dimensions, 128 evaluation prompts, 6-model AI consensus panel. Powers AISearchArena.com.

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