Hyperspectral images provide rich spectral details of the observed scene by exploiting contiguous bands. But, the processing of such images becomes heavy, due to the high dimensionality. Thus, band selection is a practice that has been adopted before any further processing takes place. Therefore, in this paper, a new unsupervised method for band selection based on clustering and neural network is proposed. A comparison with six other band selection frameworks shows the strength of the proposed method
Hyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or...
Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy com...
Hyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or...
National audienceHyperspectral images provide rich spectral details of the observed scene by exploit...
International audienceHyperspectral images provide fine details of the scene under analysis in terms...
<p>Band selection, by choosing a set of representative bands in a hyperspectral image, is an effecti...
A novel unsupervised band selection method is proposed, where adaptive clustering of spectral compon...
A novel unsupervised band selection method is proposed, where adaptive clustering of spectral compon...
International audienceIn order to alleviate the negative effect of curse of dimensionality, band sel...
A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, ...
International audienceHyperspectral images provide fine details of the scene under analysis in terms...
<p>Band selection is a kind of dimension reduction method, which tries to remove redundant bands and...
Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection te...
<p> Band selection, by choosing a set of representative bands in hyperspectral images (HSI), is con...
International audienceThe problem of band selection (BS) is of great importance to handle the curse ...
Hyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or...
Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy com...
Hyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or...
National audienceHyperspectral images provide rich spectral details of the observed scene by exploit...
International audienceHyperspectral images provide fine details of the scene under analysis in terms...
<p>Band selection, by choosing a set of representative bands in a hyperspectral image, is an effecti...
A novel unsupervised band selection method is proposed, where adaptive clustering of spectral compon...
A novel unsupervised band selection method is proposed, where adaptive clustering of spectral compon...
International audienceIn order to alleviate the negative effect of curse of dimensionality, band sel...
A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, ...
International audienceHyperspectral images provide fine details of the scene under analysis in terms...
<p>Band selection is a kind of dimension reduction method, which tries to remove redundant bands and...
Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection te...
<p> Band selection, by choosing a set of representative bands in hyperspectral images (HSI), is con...
International audienceThe problem of band selection (BS) is of great importance to handle the curse ...
Hyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or...
Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy com...
Hyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or...