Breast carcinoma is the most common form of cancer in women. Multicolour fluorescent in-situ hybridisation (m-FISH) is a common method for staging breast carcinoma. The interpretation of m-FISH images is complicated due to two effects: (i) Spectral overlap in the emission spectra of fluorochrome marked DNA probes and (ii) tissue autofluorescence. In this paper hyper-spectral images of m-FISH sa...

Super resolution-based spectral unmixing (SRSU) is a recently developed method for spectral unmixing of remotely sensed imagery, but it is too complex to implement for common users who are interested in land cover mapping. This study makes use of spatial interpolation as an alternative approach to achieve super resolution reconstruction in SRSU. An ASTER image with three spectral bands was used...

Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called...

Introduction: The objective of any linear spectral unmixing procedure is to determine the abundance at which the components represented in a predetermined end-member library are present in the observed target. This is done by modeling an observed spectrum as a linear combination of end-member spectra. Following the work of Ramsey and Christensen [1] and Feely and Christensen [2] linear unmixing...

A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information. Partial Membership Latent Dirichlet Allocation is an effective approach for spectral unmixing while representing spectral variability and leveraging spatial information. In this work, we exte...

Change detection by unmixing has been shown to provide enhanced change detection performance for hyperspectral images with respect to more traditional approaches, especially when the temporal images contain sub-pixel level changes. In a recent paper, change detection by spectral unmixing was investigated in detail and the advantages that can be gained by using such an approach were systematical...

An acceleration algorithm for spectral unmixing approach is proposed based on subset selection. The method classifies the pixels in a spectral image into accurate and approximated unmixing groups based on the similarity and dissimilarity of geomorphological features in neighboring areas. Real spectral images are used for unmixing benchmark tests for accuracy and performance verification. The re...

Spectral mixture analysis (SMA) is a very important task for hyper-spectral image analysis, in general, and subpixel data extraction, in particular. In this paper we present a new methodology for spectral unmixing, where a vector of fractions, corresponding to a set of endmembers (EMs), is estimated for each pixel in the image. The process first provides an initial estimate of the fraction vect...

Fluorescence microscopy is an essential tool for modern biological research. The wide range of available fluorophores and labeling techniques allows the creation of increasingly complex multicolored samples. A reliable separation of the different fluorescence labels is required for analysis and quantitation, but it is complicated by the significant overlap of the emission spectra. This problem ...