NASA Scientific and Technical Aerospace Reports

on status as it relates to our discipline, and share some thoughts about where we need to go.
Author
Management Planning; Mission Planning; Space Law
20040074305 NASA, Washington, DC, USA
NASA Space Sciences Strategic Planning
Crane, Philippe; New Concepts for Far-Infrared and Submillimeter Space Astronomy; April 2004, pp. 3-10; In English; See
also 20040074260; No Copyright; Avail: CASI; A02, Hardcopy
The purpose of strategic planning roadmap is to:Fulfill the strategic planning requirements; Provide a guide to the science
community in presenting research requests to NASA; Inform and inspire; Focus investments in technology and research for
future missions; and Provide the scientific and technical justification for augmentation requests.
Derived from text
Management Planning; Spaceborne Astronomy
20040074312 NASA, Washington, DC, USA
Enabling Telescopes of the Future: Long-Range Technology Investing
Thronson, Harley; New Concepts for Far-Infrared and Submillimeter Space Astronomy; April 2004, pp. 18-22; In English;
See also 20040074260; No Copyright; Avail: CASI; A01, Hardcopy
The Office of Space Science at NASA Headquarters has a current staff of about 60 professionals (aka, scientists,
engineers, budget analysts) and an annual budget of $2.5 B out of NASA s $15.0 B. About 35 missions or programs in various
stages of development or operation are managed by OSS, notable among them are Hubble Space Telescope, Mars Global
Surveyor, Mars 2001 Odyssey, Chandra X-ray Observatory, TRACE (solar observatory), Cassini (mission to Saturn), Galileo
(mission at Jupiter), and Next Generation Space Telescope. OSS has an annual technology budget of several hundred million
dollars. So, what is it that we are doing?
Derived from text
Telescopes; X Ray Astrophysics Facility; X Rays
82
DOCUMENTATION AND INFORMATION SCIENCE
Includes information management; information storage and retrieval technology; technical writing; graphic arts; and micrography. For
computer program documentation see 61 Computer Programming and Software.
20040068166 NASA Ames Research Center, Moffett Field, CA, USA
Knowledge Driven Image Mining with Mixture Density Mercer Kernels
Srivastava, Ashok N.; Oza, Nikunj; 2004; 4 pp.; In English; No Copyright; Avail: CASI; A01, Hardcopy
This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer
Kernels; which are highly nonlinear symmetric positive definite mappings from the original image space to a very high,
possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and
classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly
nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This
process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with
only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in
image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing
algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We
also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged
image data warehouse for the Earth Sciences.
Author
Knowledge Based Systems; Data Mining; Image Analysis; Image Classification
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