A hybrid algorithm for automatic detection of hyperspectral dimensionality

Abstract

Hyperspectral systems have improved significantly through recent
advancements in sensor technology, which have made possible to acquire
data with several hundred channels. These advances provide the possible
benefit of not only collecting more detailed information than previously
possible, but also of producing more accurate data. Some of the major
challenges in handling such large data sets are removing redundant
information and assuring the continued relevance of vital information to
the application at hand. For example, conventional methods for land use
and land cover classifications may not be applicable, due to the large
data volumes used to characterize hyperspectral cubes. Therefore, these
conventional methods may require a preprocessing step, namely dimension
reduction. Dimension reduction can be seen as a transformation from a
high order dimension to a low order dimension in order to conquer the
so-called "curse of the dimensionality," which eliminates data
redundancy. Principal component analysis (PCA) is one such data
reduction technique, which is often used when analyzing remotely sensed
data. In computing the principal components, the eigenvalues of the
covariance matrix of the 3D image must be computed. This can be done for
all the eigenvalues at once using the standard Jacobi method, or in
one-by-one fashion using the power method, starting with the largest
eigenvalue