Bandingkan metode

	Unmixing Hiperspektral	Faktorisasi Matriks Non-Negatif (NMF)
Bidang	Penginderaan Jauh	Pembelajaran Mesin
Keluarga	Machine learning	Latent structure
Tahun asal	2002	1999
Pencetus	Nirmal Keshava & John Mustard	Lee, D. D. & Seung, H. S.
Tipe	Sub-pixel spectral decomposition algorithm	Matrix decomposition with non-negativity constraints
Sumber perintis	Keshava, N., & Mustard, J. F. (2002). Spectral unmixing. IEEE Signal Processing Magazine, 19(1), 44–57.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Alias	Spectral Mixture Analysis, Linear Spectral Unmixing, Blind Source Separation (Hyperspectral), Hiperspektral Ayrıştırma	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Terkait	2	4
Ringkasan	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.	Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.