IEEE Trans Image Process - Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral images.

Tópicos

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Resumo

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm.

Resumo Limpo

linear spectral unmix challeng problem hyperspectr imag consist decompos observ pixel linear combin pure spectra endmemb correspond proport abund endmemb extract algorithm can employ recov spectral signatur abund estim use invers step recent work shown exploit spatial depend imag pixel can improv spectral unmix markov random field mrf classic use model spatial correl partit imag multipl class homogen abund paper propos defin mrf site use similar region region built use selfcomplementari area filter stem morpholog theori kind filter divid origin imag flat zone under pixel spectral valu mrf clear establish hierarch bayesian algorithm propos estim abund class label nois varianc correspond hyperparamet hybrid gibb sampler construct generat sampl accord correspond posterior distribut unknown paramet hyperparamet simul conduct synthet real aviri data demonstr good perform algorithm

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