Adaptive Markov Random Fields for Joint Unmixing and Segmentation of Hyperspectral Images

Unmixing hyperspectral images using Markov random fields

This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) ...

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Mia Rizkinia 1,2,†,* ID and Masahiro Okuda 1,† ID 1 Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan; [email protected] 2 Faculty of Engineering, Universitas Indonesia, Depok, Jawa Barat 16424, Indonesia * Correspondence: [email protected] † This paper is partially based on the authors’ conference paper, which is presented at the 20...

Joint Blind Deconvolution and Spectral Unmixing of Hyperspectral Images

Our interest here is spectral imaging for space object identification based upon imaging using simultaneous measurements at different wavelengths. AMOS sensors can collect simultaneous images ranging from visible to LWIR. Multiframe blind deconvolution (MFBD) has demonstrated success by acquiring near-simultaneous multiple images for reconstructing space objects, and another success has been sh...

Unsupervised Segmentation of Textured Images Using Binomial Markov Random Fields

We show how scanned images can be segmented on the basis of their stochastic nature without a priori knowledge of the number of regions or their model parameters. The region distribution is modelled by a Mesh Markov Random Field while each region is filled with textures modelled by a Binomial Markov Random Field (BMRF). A hierarchical fuzzy clustering method is used to estimate the BMRF paramet...

Segmentation of magnetic resonance images using fuzzy Markov random fields