Vertaile menetelmiä

	Hyperspectral Unmixing	Pikselipohjainen kuvien luokittelu
Tieteenala	Kaukokartoitus	Kaukokartoitus
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2002	2007
Kehittäjä	Nirmal Keshava & John Mustard	Remote-sensing classification literature
Tyyppi	Sub-pixel spectral decomposition algorithm	Supervised/unsupervised spectral image classification
Alkuperäislähde	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.
Rinnakkaisnimet	Spectral Mixture Analysis, Linear Spectral Unmixing, Blind Source Separation (Hyperspectral), Hiperspektral Ayrıştırma	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma
Liittyvät	2	2
Tiivistelmä	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.