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The analysis of remotely sensed spectral imagery has a variety of applications in both the public and private sectors, including tracking urban development, monitoring the spread of diseased crops, and mapping environmental disasters. The high spatial and spectral resolutions in hyperspectral imagery (HSI) make it particularly desirable for these types of analyses, as HSI sensors capture “color” information...
Hyperspectral image data are traditionally analyzed using statistical models. However, as the spatial and spectral resolutions of the images improve as a result of advances in sensor technology, the data no longer maintain a Gaussian distribution; this is due to increased material diversity in the scene, i.e., clutter. This causes many statistical assumptions about the data — and subsequently, the...
For many applications in spectral image analysis, the quantitative model used to describe the data is based on first and second order statistics, linear mixture models (i.e., the convex hull), and / or linear subspaces. An example of this for anomaly detection is the well known RX algorithm, a statistical measure of the anomalousness of individual pixels when compared to the mean and covariance of...
Many techniques from graph theory and network theory have been applied to traditional images, and some techniques are now being applied to spectral imagery. Contrary to the typical approaches of utilizing the first order statistics, mixture models, and linear subspaces, the methods described in this paper utilize the spectral data structure to generate a graph representation of the image. By ignoring...
In recent years, the Isomap method has been widely used for making nonlinearly reduction for hyperspectral image. However, during the construction process of the short path graph, the boundary points, which are not noise points, have always been omitted for the consideration of the stability of the graph. In the paper, we introduce the PLS method to repair and simulate the manifold coordinates of...
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