Remotely sensed multispectral image data are found in grouped form with (say) s spectral components (bands). In this study, a practical method for constructing a mixture model or the probability density function of the mixture of k (3 =< k =< s) normal distributions for a spectral class is given. A new method for estimation of the mixing proportions of spectral components (bands) in the remotely sensed multispectral image data is proposed with the assumption that the spectral component (band) means are different from each other. © 2000 Taylor & Francis Group, LLC
Abstract — Many available techniques for spectral mixture analysis involve the separation of mixed p...
International audienceHyperspectral imagery collected from airborne or satellite sources inevitably ...
A multispectral classification algorithm is developed for classifying remotely-sensed data extracted...
WOS: A1995RL80200004The mixture of three normal distributions is proposed as a model for an area tha...
The mixture of three normal distributions is proposed as a model for an area that we call a ‘class’ ...
The objective of this dissertation is to investigate all the necessary components in spectral mixtur...
Mixture processing of remotely sensed multispectral scanner data involves estimating the percent cov...
Answering to metrological constraints typically required in the context of industrial and medical ap...
L inear spectral mixture analysis can be used to model the.spectral variability in multi- or hypersp...
A new supervised classification method is developed for quantitative analysis of remotely-sensed mul...
It iswell-known that there is a strong relation between class definition precision and classificatio...
The estimation of proportions of classes in the mixed pixels of multichannel imagery data is conside...
Abstract. As a supplement or an alternative to classification of hyperspectral image data linear and...
Recently, remotely sensed multispectral data have been proved to be very useful for many application...
International audienceHyperspectral imagery collected from airborne or satellite sources inevitably ...
Abstract — Many available techniques for spectral mixture analysis involve the separation of mixed p...
International audienceHyperspectral imagery collected from airborne or satellite sources inevitably ...
A multispectral classification algorithm is developed for classifying remotely-sensed data extracted...
WOS: A1995RL80200004The mixture of three normal distributions is proposed as a model for an area tha...
The mixture of three normal distributions is proposed as a model for an area that we call a ‘class’ ...
The objective of this dissertation is to investigate all the necessary components in spectral mixtur...
Mixture processing of remotely sensed multispectral scanner data involves estimating the percent cov...
Answering to metrological constraints typically required in the context of industrial and medical ap...
L inear spectral mixture analysis can be used to model the.spectral variability in multi- or hypersp...
A new supervised classification method is developed for quantitative analysis of remotely-sensed mul...
It iswell-known that there is a strong relation between class definition precision and classificatio...
The estimation of proportions of classes in the mixed pixels of multichannel imagery data is conside...
Abstract. As a supplement or an alternative to classification of hyperspectral image data linear and...
Recently, remotely sensed multispectral data have been proved to be very useful for many application...
International audienceHyperspectral imagery collected from airborne or satellite sources inevitably ...
Abstract — Many available techniques for spectral mixture analysis involve the separation of mixed p...
International audienceHyperspectral imagery collected from airborne or satellite sources inevitably ...
A multispectral classification algorithm is developed for classifying remotely-sensed data extracted...