endmemberanalüüsi

Endmember analysis is a technique used in various scientific fields, particularly in remote sensing and geochemistry, to identify and quantify the pure components (endmembers) that make up a mixed signal. The fundamental principle is that any observed spectrum or mixture can be represented as a linear combination of these underlying pure components. The goal of endmember analysis is to determine the spectral signatures of these endmembers and their relative abundances within the mixture.

In remote sensing, endmember analysis is often applied to hyperspectral imagery. Each pixel in a hyperspectral image contains a spectrum representing the light reflected from a specific ground cover. Endmember analysis aims to identify the spectra of pure materials like vegetation, soil, and water, and then estimate the proportion of each endmember present in every pixel. This allows for the creation of abundance maps, showing the spatial distribution of different materials.

The process typically involves algorithms that either assume the endmembers are present within the data itself (unsupervised endmember extraction) or require prior knowledge of the endmember spectra (supervised endmember selection). Common unsupervised methods include Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and various iterative algorithms like Iterative Endmember Selection (IES) or Multiple Endmember Spectral Mixture Analysis (MESMA). The output of endmember analysis provides a simplified and interpretable representation of complex spectral data, facilitating tasks such as material mapping, biophysical parameter retrieval, and environmental monitoring.