IEEE Trans Image Process - Blind spectral unmixing based on sparse nonnegative matrix factorization.

Tópicos

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Resumo

Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing (SU), which aims at obtaining the endmembers and corresponding fractional abundances, knowing only the collected mixing spectral data. It is noted that the abundance may be sparse (i.e., the endmembers may be with sparse distributions) and sparse NMF tends to lead to a unique result, so it is intuitive and meaningful to constrain NMF with sparseness for solving SU. However, due to the abundance sum-to-one constraint in SU, the traditional sparseness measured by L0/L1-norm is not an effective constraint any more. A novel measure (termed as S-measure) of sparseness using higher order norms of the signal vector is proposed in this paper. It features the physical significance. By using the S-measure constraint (SMC), a gradient-based sparse NMF algorithm (termed as NMF-SMC) is proposed for solving the SU problem, where the learning rate is adaptively selected, and the endmembers and abundances are simultaneously estimated. In the proposed NMF-SMC, there is no pure index assumption and no need to know the exact sparseness degree of the abundance in prior. Yet, it does not require the preprocessing of dimension reduction in which some useful information may be lost. Experiments based on synthetic mixtures and real-world images collected by AVIRIS and HYDICE sensors are performed to evaluate the validity of the proposed method.

Resumo Limpo

nonneg matrix factor nmf wide use method blind spectral unmix su aim obtain endmemb correspond fraction abund know collect mix spectral data note abund may spars ie endmemb may spars distribut spars nmf tend lead uniqu result intuit meaning constrain nmf spars solv su howev due abund sumtoon constraint su tradit spars measur llnorm effect constraint novel measur term smeasur spars use higher order norm signal vector propos paper featur physic signific use smeasur constraint smc gradientbas spars nmf algorithm term nmfsmc propos solv su problem learn rate adapt select endmemb abund simultan estim propos nmfsmc pure index assumpt need know exact spars degre abund prior yet requir preprocess dimens reduct use inform may lost experi base synthet mixtur realworld imag collect aviri hydic sensor perform evalu valid propos method

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