Porovnat metody

	Hyperspectral Unmixing	Pixel-based image classification
Obor	Dálkový průzkum Země	Dálkový průzkum Země
Rodina	Machine learning	Machine learning
Rok vzniku	2002	2007
Tvůrce	Nirmal Keshava & John Mustard	Remote-sensing classification literature
Typ	Sub-pixel spectral decomposition algorithm	Supervised/unsupervised spectral image classification
Původní zdroj	Keshava, N., & Mustard, J. F. (2002). Spectral unmixing. IEEE Signal Processing Magazine, 19(1), 44–57.	Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.
Další názvy	Spectral Mixture Analysis, Linear Spectral Unmixing, Blind Source Separation (Hyperspectral), Hiperspektral Ayrıştırma	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma
Příbuzné	2	2
Shrnutí	Hyperspectral unmixing is a signal processing technique that decomposes each pixel of a hyperspectral image into a collection of pure material spectra (endmembers) and their corresponding fractional abundances. Because sensor resolution often causes multiple land-cover types to co-occupy a single pixel, unmixing recovers sub-pixel compositional information that conventional classification cannot. Keshava and Mustard (2002) provided the foundational signal-processing framework that unified prior geological and remote-sensing work under a rigorous linear mixture model.	Pixel-based image classification is a fundamental remote-sensing technique that assigns each individual pixel in a satellite or aerial image to a thematic land-cover category based solely on its spectral values across multiple bands. Systematically surveyed and formalized by Lu and Weng (2007), the approach encompasses both supervised methods—where labeled training samples guide the classifier—and unsupervised clustering approaches that discover natural spectral groupings without prior labels.