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Spectral mixtures observed in hyperspectral imagery often display nonlinear mixing effects. Since most traditional unmixing techniques are based upon the linear mixing model, they perform poorly in finding the correct endmembers and their abundances in the case of nonlinear spectral mixing. In this paper, we present an unmixing algorithm that is capable of extracting endmembers and determining their...
In this paper, we present an unmixing algorithm that is capable to determine endmembers and their abundances in hyperspectral imagery under non-linear mixing assumptions. The algorithm is an based upon the popular N-findR method, but uses distances between points in spectral space instead of the spectral values. These distances are defined as shortest-path distances in a nearest-neighbor graph, hereby...
It is generally understood that interpreting depth from monoscopic images inherently induces the problem of object segmentation and context recognition. A common way to deal with this complexity is to focus on one aspect (e.g. segmentation) and largely constrain or skip the other (e.g. scene classification). In our approach, we practically reconcile the two paths. We start by defining several depth...
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