6 research outputs found
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
The Fifth NASA/DOD Controls-Structures Interaction Technology Conference, part 2
This publication is a compilation of the papers presented at the Fifth NASA/DoD Controls-Structures Interaction (CSI) Technology Conference held in Lake Tahoe, Nevada, March 3-5, 1992. The conference, which was jointly sponsored by the NASA Office of Aeronautics and Space Technology and the Department of Defense, was organized by the NASA Langley Research Center. The purpose of this conference was to report to industry, academia, and government agencies on the current status of controls-structures interaction technology. The agenda covered ground testing, integrated design, analysis, flight experiments and concepts
Challenges and Open Questions of Machine Learning in Computer Security
This habilitation thesis presents advancements in machine learning for computer security,
arising from problems in network intrusion detection and steganography.
The thesis put an emphasis on explanation of traits shared by steganalysis, network intrusion
detection, and other security domains, which makes these domains different from
computer vision, speech recognition, and other fields where machine learning is typically
studied. Then, the thesis presents methods developed to at least partially solve the identified
problems with an overall goal to make machine learning based intrusion detection
system viable. Most of them are general in the sense that they can be used outside intrusion
detection and steganalysis on problems with similar constraints.
A common feature of all methods is that they are generally simple, yet surprisingly
effective. According to large-scale experiments they almost always improve the prior art,
which is likely caused by being tailored to security problems and designed for large volumes
of data.
Specifically, the thesis addresses following problems:
anomaly detection with low computational and memory complexity such that efficient
processing of large data is possible;
multiple-instance anomaly detection improving signal-to-noise ration by classifying
larger group of samples;
supervised classification of tree-structured data simplifying their encoding in neural
networks;
clustering of structured data;
supervised training with the emphasis on the precision in top p% of returned data;
and finally explanation of anomalies to help humans understand the nature of anomaly
and speed-up their decision.
Many algorithms and method presented in this thesis are deployed in the real intrusion
detection system protecting millions of computers around the globe
Space Communications: Theory and Applications. Volume 3: Information Processing and Advanced Techniques. A Bibliography, 1958 - 1963
Annotated bibliography on information processing and advanced communication techniques - theory and applications of space communication