A hyperspectral data cube is typically composed of about 100 to 200 spectral measurements for each spatial element of an imaged scene. They form the original set of the spectral features. From there, new linear or nonlinear transformed features can be generated in spectral space. In image space, spatial texture features, such as contrast, homogeneity and energy, can be derived and structure enhanced new features can be obtained by applying Morphological filtering.
Shape and size
related features are available via object-based operations.Is
creating more features better? Is using a large
number of features in machine learning a good practice? In
this talk, issues in effective features generation and selection will
be addressed. An information class separability measure in cluster
space will be introduced. The reduced subset of features is expected
to minimize redundancies, enhance class separability and avoid the
Hughes phenomenon. Feature integration approaches will be discussed
briefly as well.
Jia received the B. Eng. degree from Beijing University of
Posts and Telecommunications, Beijing, China, in 1982 and the Ph.D
degree in Electrical Engineering from The University of New South
Wales, Australia, in 1996. Her thesis was titled ‘Classification
techniques for hyperspectral remote sensing image data’. Since then
she has continued her research in image processing, data analysis and
remote sensing applications. The projects she has been involved in
and supervised range from image registration, data compression,
feature reduction, to spectral-spatial based classification. Subpixel
mapping has been addressed in recent years via spectral unmixing
techniques and super resolution reconstruction approaches.
She is the co-author of the remote sensing textbook, Remote Sensing Digital Image Analysis, Berlin, Germany: Springer-Verlag, 3rd (1999) and 4th (2006) eds. She is as an Associate Editor of IEEE Geoscience and Remote Sensing, and a member of the International Committee for Imaging Science. Dr. Jia is a Guest Professor of Beijing Normal University.