Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing

Abstract

Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two nonnegative matrices, i.e., endmember and abundance matrices. Because the objective function of NMF is the traditional least-squares function, NMF is sensitive to noise. In order to improve the robustness of NMF, this paper proposes a maximum likelihood estimation (MLE) based NMF model (MLENMF) for unmixing of hyperspectral images (HSIs), which substitutes the least-squares objective function in traditional NMF by a robust MLE-based loss function. Experimental results on a simulated and two widely used real hyperspectral data sets demonstrate the superiority of our MLENMF over existing NMF methods.

Research on the Semantic Analysis Method of Translation Corpus Based on Natural Language Processing

“…e basic idea of this method is to extract all translation phrase rules consistent with word alignment according to the alignment results of bilingual data. e probability estimation of phrase-based translation rules generally adopts count based maximum likelihood estimation [10,11]. e traditional neural network is trained mainly through the back propagation of errors.…”