Spectral unmixing using nonnegative matrix factorization with smoothed L0 norm constraint
نویسندگان
چکیده
Sparse nonnegative matrix factorization (NMF) is exploited to solve spectral unmixing. Firstly, a novel model of sparse NMF is proposed, where the smoothed L0 norm is used to control the sparseness of the factors corresponding to the abundances. Thus, one need not set the degree of the sparseness in prior any more. Then, a gradient based algorithm NMF-SL0 is utilized to solve the proposed model, where the learning rate is adaptively selected. Simulations for synthetic dataset and real dataset show the validity of the proposed method.
منابع مشابه
Sparsity-constrained Nonnegative Matrix Factorization
Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L1 regularizer. Nonetheless, recent stud...
متن کاملNonnegative Matrix Factorization on Orthogonal Subspace with Smoothed L0 Norm Constrained
It is known that the sparseness of the factor matrices by Nonnegative Matrix Factorization can influence the clustering performance. In order to improve the ability of the sparse representations of the NMF, we proposed the new algorithm for Nonnegatie Matrix Factorization, coined nonnegative matrix factorization on orthogonal subspace with smoothed L0 norm constrained, in which the generation o...
متن کاملجداسازی طیفی و مکانی تصاویر ابرطیفی با استفاده از Semi-NMF و تبدیل PCA
Unmixing of remote-sensing data using nonnegative matrix factorization has been considered recently. To improve performance, additional constraints are added to the cost function. The main challenge is to introduce constraints that lead to better results for unmixing. Correlation between bands of Hyperspectral images is the problem that is paid less attention to it in the unmixing algorithms. I...
متن کاملDistributed Unmixing of Hyperspectral Data With Sparsity Constraint
Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which w...
متن کاملArea-Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization
To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. In the proposed method, the spatial correlation property between two adjacent areas is expressed by a priori probability density function, and the endmembers extracted from one of t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009