Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery
Two of the most challenging issues in the unsupervised linear spectral mixture analysis
(ULSMA) are: 1) determining the number of signatures to form a linear mixing model; and 2)
finding the signatures used to unmix data. These two issues do not occur in supervised
LSMA since the target signatures are assumed to be known a priori. With recent advances in
hyperspectral sensor technology, many unknown and subtle signal sources can now be
uncovered and revealed and such signal sources generally cannot be identified by prior …
(ULSMA) are: 1) determining the number of signatures to form a linear mixing model; and 2)
finding the signatures used to unmix data. These two issues do not occur in supervised
LSMA since the target signatures are assumed to be known a priori. With recent advances in
hyperspectral sensor technology, many unknown and subtle signal sources can now be
uncovered and revealed and such signal sources generally cannot be identified by prior …
Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery
One of the most challenging issues in unsupervised linear spectral mixture analysis (LSMA)
is how to obtain unknown knowledge of target signatures referred to as virtual endmembers
(VEs) directly from the data to be processed. This issue has never arisen in supervised
LSMA where the VEs are either assumed to be known a priori or can be provided by visual
inspection. With the recent advent of hyperspectral sensor technology many unknown and
subtle signal sources can be uncovered and revealed without prior knowledge. This paper …
is how to obtain unknown knowledge of target signatures referred to as virtual endmembers
(VEs) directly from the data to be processed. This issue has never arisen in supervised
LSMA where the VEs are either assumed to be known a priori or can be provided by visual
inspection. With the recent advent of hyperspectral sensor technology many unknown and
subtle signal sources can be uncovered and revealed without prior knowledge. This paper …
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