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Incorporating spatial information in spectral unmixing: A review
Spectral unmixing is the process of decomposing the spectral signature of a mixed pixel into
a set of endmembers and their corresponding abundances. Endmembers are spectra of the …
a set of endmembers and their corresponding abundances. Endmembers are spectra of the …
[HTML][HTML] Hyperspectral image spatial super-resolution via 3D full convolutional neural network
Hyperspectral images are well-known for their fine spectral resolution to discriminate
different materials. However, their spatial resolution is relatively low due to the trade-off in …
different materials. However, their spatial resolution is relatively low due to the trade-off in …
A survey of methods incorporating spatial information in image classification and spectral unmixing
Over the past decade, the incorporation of spatial information has drawn increasing attention
in multispectral and hyperspectral data analysis. In particular, the property of spatial …
in multispectral and hyperspectral data analysis. In particular, the property of spatial …
Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing
In recent years, blind source separation (BSS) has received much attention in the
hyperspectral unmixing field due to the fact that it allows the simultaneous estimation of both …
hyperspectral unmixing field due to the fact that it allows the simultaneous estimation of both …
[HTML][HTML] Comparison of CNN algorithms on hyperspectral image classification in agricultural lands
Several versions of convolutional neural network (CNN) were developed to classify
hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral …
hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral …
Regional clustering-based spatial preprocessing for hyperspectral unmixing
Hyperspectral unmixing is an important technique for remote sensing image exploitation. It
aims to decompose a mixed pixel into a collection of spectrally pure components (called …
aims to decompose a mixed pixel into a collection of spectrally pure components (called …
Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data
Spectral unmixing amounts at estimating the abundance of pure spectral signatures (called
endmembers) in each mixed pixel of a hyperspectral image, where mixed pixels arise due to …
endmembers) in each mixed pixel of a hyperspectral image, where mixed pixels arise due to …
[PDF][PDF] Foreword to the special issue on spectral unmixing of remotely sensed data
MORE than two decades after the first efforts toward the application of spectral mixture
analysis techniques to remotely sensed data [1],[2], effective spectral unmixing still remains …
analysis techniques to remotely sensed data [1],[2], effective spectral unmixing still remains …
[HTML][HTML] Estimating fractional vegetation cover from multispectral unmixing modeled with local endmember variability and spatial contextual information
Vegetation fractional cover (fCover) is an important canopy structural variable for
understanding the climate-vegetation feedback. Trees and non-tree vegetation may respond …
understanding the climate-vegetation feedback. Trees and non-tree vegetation may respond …
Spectral variability augmented sparse unmixing of hyperspectral images
Spectral unmixing expresses the mixed pixels existing in hyperspectral images as the
product of endmembers and their corresponding fractional abundances, which has been …
product of endmembers and their corresponding fractional abundances, which has been …