This paper describes a new method for linear spectral mixture analysis. Endmember spectra are only used as initial values in a least squares adjustment according to a Gauss-Markov model. The observations of the adjustment are the spectra of pixels in a pre-defined neighbourhood, the important result are improved endmember spectra. In a subsequent step the endmember percentages per pixel are derived using the MESMA approach. Using a level 1B ASTER satellite image of Burkina Faso the accuracy of the new model is compared to that of a standard unmixing approach. The new model predicts vegetation components considerably more accurate
Abstract Spectral unmixing is an important task for remotely sensed hyper-spectral data exploitation...
This report documents the algorithms used in the program MIXMOD to analyse mixed pixel data (assumin...
International audienceThis paper proposes a novel mixing model that incorporates spectral variabilit...
L inear spectral mixture analysis can be used to model the.spectral variability in multi- or hypersp...
Spectral mixture analysis is a widely used method to determine the sub-pixel abundance of vegetation...
Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fraction...
Abstract—Spectral mixture analysis provides an efficient mechanism for the interpretation and classi...
There are a considerable number of mixed pixels in remotely sensed images. Different sub-pixel analy...
The objective of this dissertation is to investigate all the necessary components in spectral mixtur...
Abstract — Many available techniques for spectral mixture analysis involve the separation of mixed p...
In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed s...
End-member extraction could be considered as the most challenging stage of the spectral unmixing pro...
Abstract. As a supplement or an alternative to classification of hyperspectral image data linear and...
Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urb...
Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember varia...
Abstract Spectral unmixing is an important task for remotely sensed hyper-spectral data exploitation...
This report documents the algorithms used in the program MIXMOD to analyse mixed pixel data (assumin...
International audienceThis paper proposes a novel mixing model that incorporates spectral variabilit...
L inear spectral mixture analysis can be used to model the.spectral variability in multi- or hypersp...
Spectral mixture analysis is a widely used method to determine the sub-pixel abundance of vegetation...
Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fraction...
Abstract—Spectral mixture analysis provides an efficient mechanism for the interpretation and classi...
There are a considerable number of mixed pixels in remotely sensed images. Different sub-pixel analy...
The objective of this dissertation is to investigate all the necessary components in spectral mixtur...
Abstract — Many available techniques for spectral mixture analysis involve the separation of mixed p...
In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed s...
End-member extraction could be considered as the most challenging stage of the spectral unmixing pro...
Abstract. As a supplement or an alternative to classification of hyperspectral image data linear and...
Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urb...
Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember varia...
Abstract Spectral unmixing is an important task for remotely sensed hyper-spectral data exploitation...
This report documents the algorithms used in the program MIXMOD to analyse mixed pixel data (assumin...
International audienceThis paper proposes a novel mixing model that incorporates spectral variabilit...