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...

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...

Spectral unmixing is an important problem in hyperspectral data exploitation. It amounts at characterizing the mixed spectral signatures collected by an imaging instrument in the form of a combination of pure spectral constituents (endmembers), weighted by their correspondent abundance fractions. Linear spectral unmixing is a popular technique in the literature which assumes linear interactions...

Spectral unmixing is a type of hyperspectral imagery (HSI) sub-pixel analysis where the constituent spectra and abundances within the pixel are identified. However, validating the results obtained from spectral unmixing is very difficult due to a lack of real-world data and ground-truth information associated with these real-world images. Real HSI data is preferred for validating spectral unmix...

Hyperspectral imagery (HSI) unmixing is a process that decomposes pixel spectra into a collection of constituent spectra (endmembers) and their correspondent abundance fractions. Without knowing any knowledge of HSI data, the unmixing problem is transformed into a blind source separation (BSS) problem. Several methods have been proposed to deal with the problem, like independent component analy...

Abstract—Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no info...

-Advanced Very High Resolution Radiometer imagery provides frequent and low-cost coverage of the earth, but its coarse spatial resolution (-1.1 km by 1.1 km) does not lend itself to standard techniques of automated categorization of land cover classes because the pixels are generally mixed; that is, the extent of the pixel includes several land use/cover classes. Unmixing procedures were develo...

Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquire...

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...