63 research outputs found
Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation
The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for
hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA
provides a model for a hyperspectral image analysis that accounts for spectral
variability and incorporates spatial information through the use of
superpixel-based 'documents.' In our application of PM-LDA, we employ the
Normal Compositional Model in which endmembers are represented as Normal
distributions to account for spectral variability and proportion vectors are
modeled as random variables governed by a Dirichlet distribution. The use of
the Dirichlet distribution enforces positivity and sum-to-one constraints on
the proportion values. Algorithm results on real hyperspectral data indicate
that PM-LDA produces endmember distributions that represent the ground truth
classes and their associated variability
Multiple Instance Hybrid Estimator for Learning Target Signatures
Signature-based detectors for hyperspectral target detection rely on knowing
the specific target signature in advance. However, target signature are often
difficult or impossible to obtain. Furthermore, common methods for obtaining
target signatures, such as from laboratory measurements or manual selection
from an image scene, usually do not capture the discriminative features of
target class. In this paper, an approach for estimating a discriminative target
signature from imprecise labels is presented. The proposed approach maximizes
the response of the hybrid sub-pixel detector within a multiple instance
learning framework and estimates a set of discriminative target signatures.
After learning target signatures, any signature based detector can then be
applied on test data. Both simulated and real hyperspectral target detection
experiments are shown to illustrate the effectiveness of the method
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