Langley's mission is accomplished by performing innovative research relevant to national needs and Agency goals, transferring technology to users in a timely manner, and providing development support to other United States Government Agencies, industry, other NASA Centers, the educational community, and the local community. This Editorial reports the efforts of the NASA Soil Moisture Active Passive (SMAP) mission to integrate applications with science and engineering i. The National Research Council (NRC) recently highlighted the dual role of NASA to support both science and applications in planning Earth observations. USDA-ARS?s Scientific Manuscript database Excellent training for NASA scientists and engineers.Ĭonnecting NASA science and engineering with earth science applications Serve as a technology development platform. The NASA Balloon Program provides low-cost, quick response, near space access to NASAs science Community for conducting Cutting Edge Science Investigations.
Topics covered include Space Shuttle flights, understanding the Universe and its origins, understanding the Earth and its environment, air and space transportation, using space to make America more competitive, using space technology an Earth, strengthening America's education in science and technology, the space station, and human exploration of the solar system. Highlights of NASA research from 1986 to 1988 are discussed. By giving more visibility to results of Solar System research, our goal is to encourage Over the past decade, there has been a trend of flat budgets for Research and Analysis activities. The site, /, is one of NASA's most visited. Some spotlights will also be converted into feature stories for the Solar System Exploration website so the public, too, can learn about exciting new research. In addition, the science results may also be incorporated into briefing material for the Office of Management and Budget and congressional staffers. The information from some of these highlights can serve as a basis to bring Principal Investigators to NASA Headquarters for exposure to media through Space Science Updates on NASA television. She will then present the exciting findings to Associate Administrator for Space Science, Dr. After a writer interviews the scientist, a brief Power Point presentation that encapsulates their work will be given to Dr. Each month, one researcher's work will be chosen as a science spotlight. The information is available to a limited number of reviewers and writers at JPL. There they provide their contact information, briefly describe their research, and upload any associated images or graphics. The site ( /spotlight/ - Username: your email address Password: sse) is an online submission area where NASA-funded scientists can upload the results of their research. In support of this effort, a new feature has been developed for the NASA Headquarters Solar System Exploration Division web site whereby researchers can provide a synopsis of their current research results. But I am not sure if I am right or not.An effort is underway to provide greater visibility within NASA headquarters, and to those who provide funding to NASA, of the outstanding work that is being performed by scientists involved in the Solar System Exploration Research and Analysis Programs, most of whom are DPS members. Since I use sparse representation and dictionary concept then reducing the dimension of $d$ (observation pixels for individual classes) is more make sense rather than reducing the number of features ( $B$). But my question is should I reduce the number of training pixels(observation=d$$) or reduce the variable dimension ($B$)? I have implemented the PCA in Octave and project my data on that particular low dimension.
However, my goal is to apply PCA on hyperspectral satellite imagery like this. Therefore I want to apply PCA to individual sub-dictionary in order to form the main dictionary. Now I am trying to construct the $D$ by this mean that the classes (sub-dictionaries) are well separated. So consider $D$ as a dictionary with $d\times B$ dimension where $d=3000$ is the number of samples and $B=200$ is the number of band/channel. My goal is to classify a hyperspectral image using sparse representation by the linear combination concept which is as follow: I have been studying the concept of PCA and its implementation for dimensionality reduction for more than 1 month.