Remote Sensing Center

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Remote Sensing Center
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Established September 2006
Type Graduate school
Location Monterey, California, USA
Website [1]

The Remote Sensing Center (RSC) at the Naval Postgraduate School was established to bring together a range of capabilities and expertise to address problems of military and intelligence importance, as well as environmental and civil concerns. It is specialized in a variety of remote sensing technologies designed to enable people to look beyond the range of human vision in range or in spectral perception.

Mission[edit]

Members of the RSC come from the physics, electrical and computer engineering, computer science, meteorology, and oceanography departments. They are collaborating to develop new remote sensing systems, as well as use and exploit current systems in air and space. It is part of a larger activity in the Monterey Bay area that provides expertise in topical areas outside the technical disciplines available at NPS.

The Naval Postgraduate School, and specifically the Remote Sensing Center, has the ability to handle classified data, as well as access to a Sensitive Compartmented Information Facility (SCIF) that is fully equipped with comms, storage, and processing capabilities. The RSC has pre-established cooperative research with government, academia, and industry in the remote sensing sector ranging from local to international partners. Highly experienced military officers, intelligence analysts, and faculty are a critical part of the NPS research staff.

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Projects[edit]

Lidar[edit]

Lidar (LIght raDAR) works as an optical analog to radar in the visible spectrum of light with advantages related to the smaller wavelengths of the laser pulse. Lidar ranges in wavelength from ultra-violet (0.3-0.45 µm) to visible (0.45-0.70 µm) to the infrared (1-15 µm). Lidar can detect much smaller particles than radar in the atmosphere (which cannot detect things smaller than cloud particles), and thus can be used for aerosol detection.

The raw form of data is a set of x,y,z coordinate points. With recent advances, resolution has improved dramatically. Raw data can be processed to remove unwanted areas or features. Outputs such as topographic maps with contour lines can also be derived from lidar. Programs to manipulate lidar data include ENVI, ERDAS IMAGINE, ArcInfo, and ESRI ArcView (with 3D analyst ext.) One useful derivation of lidar data is the DEM (Digital Elevation Model). DEMs are displayed in a raster format with a matrix. The DEM has a specified cell size that corresponds to the earth’s surfaces. The cell contains the average elevation of the points within it.

The Remote Sensing Center is planning research projects that undertake the modeling and testing of analytical processing and using more fieldwork to obtain ground-truth measurements. Projects have been completed and are currently underway in terrain classification including Elkhorn Slough and hidden trail identification. Other future projects include a collaboration with the MOdeling, Virtual Environments, and Simulation (MOVES) institute on lidar standards for data structure and visualization tools and modeling new lidar analysis tools.

Spectral Imagery Analysis[edit]

Spectral imagery measures the spectral character of materials within the visible range and beyond. Two objects may appear visually identical but may be distinguished through examination of their spectral properties. Computer software can use a color scheme to make them visible.

A subset of spectral imagery, hyperspectral imaging data, is produced when "solar electromagnetic energy reflected from the earth's surface is dispersed into many contiguous narrow spectral bands by an airborne spectrometer" (Stefanou, 1997, p. 2). Our current research and projects include environmental mapping, target detection, and change detection.

The Remote Sensing Center works with airborne and satellite systems including IKONOS/Quickbird multispectral imagery (MSI), and airborne hyperspectral imaging (HSI) systems including AVIRIS, HYDICE, CASI, and HYMAP. Classification and analysis, including atmospheric compensation is performed using standard industry research tools; notably ENVI and ERDAS Imagine. The RSC has acquired a polarimetric camera for expanding experimentation in the visible spectrum.

Intelligence[edit]

The Remote Sensing Center benefits from the secure facilities at NPS. Having the ability to process classified data with an on-site, fully equipped Sensitive Compartmented Information Facility (SCIF) allows students and faculty to pursue lines of research and work with technologies unavailable to the public.

The sustained efforts of fully funded graduate students, both military and civilian with an average of eight to ten years of field experience, have conducted research in an array of topics related to remote sensing.

  • Current projects include:
Helicopter Brownout (terrain classification) using National Technical Means (NTM)
Maritime Domain Awareness (ASW)
Snow/Ice study with UCSB using NTM

Degree Programs[edit]

The Remote Sensing Research and Education Program (RS-REP) is an interdisciplinary program to promote Remote Sensing technical education and research advancement to ensure that the Intelligence Community is fully supported for technology evolution. The combined agency program is sponsored by the National Geospatial-Intelligence Agency in support of its Image Analysts.

Classified Education and Research

One of the unique benefits of education opportunities at the Naval Postgraduate School is access to classified data, as well as the ability to produce classified theses. NPS sets the bar for classified intelligence research as well as academic excellence in a secure environment. NPS is fully equipped with a SCIF, a Classified Library, and many faculty members cleared to the Top Secret (SCI) level, commodities unavailable at most civilian universities.

Curriculum and Program Description

The curriculum course matrix was approved by the NPS Academic Council New Curriculum Committee in January 2009.

The curriculum is designed to be modular with three elements of interdisciplinary study: foundation technical skills, Remote Sensing technology and techniques, and intelligence applications. Several of the courses are taught at the TS (SCI) level. Current clearance is not required for admission, however an active TS (SCI) clearance must be obtained by the time coursework is started as there is not time to complete the process once classes have begun. Ideally students are involved in research requested and funded by their sponsoring agency to ensure that the research products from the program are relevant, viable and timely.

The degree program is 12 months (4 quarters) and includes thesis research. All students are required to complete a thesis for graduation (IAW with their sponsoring agency's research needs).

Sponsorship

The Remote Sensing Center is looking to expand interest in Remote Sensing education throughout the RS product stakeholders within the government, military and intelligence communities. Agencies interested in sending their employees (or hiring employees via the Scholarship for Service programs) are encouraged to contact us to discuss their education needs.

Partners[edit]

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  • University Partners
University of California at Santa Barbara (http://www.ucsb.edu/)
University of California at Santa Cruz (http://www.ucsc.edu/public/)
California State University at Monterey Bay (http://csumb.edu/)
  • Community Science Partners
Elkhorn Slough Foundation (http://www.elkhornslough.org/)
Moss Landing Marine Laboratory (http://www.mlml.calstate.edu/)
Stanford’s Hopkins Marine Institute (http://www-marine.stanford.edu/)
Monterey Bay Aquarium Research Institute (http://www.mbari.org/)
Fleet Numerical Meteorology and Oceanography Center (https://www.fnmoc.navy.mil/public/)
National Laboratories
Lawrence Livermore National Laboratory (https://www.llnl.gov/)
Argonne National Laboratory (http://www.anl.gov/)
  • Government Agencies
National Oceanic and Atmospheric Administration (http://www.noaa.gov/)
United States Geological Survey (http://www.usgs.gov/)
United States Naval Research Laboratory (http://www.nrl.navy.mil/)
DIA Advanced RADAR Center
National Geospatial-Intelligence Agency (NGA) (http://www1.nga.mil/Pages/Default.aspx)
National Reconnaissance Office (NRO) (http://www.nro.gov/)

Members[edit]

  • Faculty and Staff
Dr. Richard Christopher Olsen: Executive Director, Professor in the Physics Department (http://www.nps.edu/faculty/olsen/index.html)
Dr. Philip A. Durkee: Associate Director, Chair of the Meteorology Department
Mrs. Angie Kim: Technical Projects Director
Ms. Jean Ferreira: Operations Manager
Mr. David Trask: NPS MASINT Chair (Colonel, USAF ret.)
Mr. Kirk Benson: Server Administrator
Research Associates: Dr. Chad Brodel, Ms. Krista Lee
Research Assistants: Ms. Chelsea Esterline, Ms. Sarah Carlisle, Mr. Cody Lanning
  • Affiliated Faculty
Dr. Brett Borden
Dr. Jeffrey Paduan (http://www.oc.nps.edu/~paduan/)
Dr. Mathias Kolsch (http://www.movesinstitute.org/~kolsch/)

References[edit]

  • R.C. Olsen, Remote Sensing from Air And Space (SPIE Press Monograph Vol. PM162).
  • The Use Of Commercial Remote Sensing In Predicting Helicopter Brownout Conditions

Anthony W Davis Jr.- Lieutenant, United States Navy September 2007 Advisor: Richard Olsen Second Reader: David Trask (http://www.nps.edu/Faculty/Olsen/Student_theses/07Sep_Davis.pdf)

  • A Signal Processing Perspective of Hyperspectral Imagery Analysis Techniques

Marcus Stavros Stefanou, Electrical Engineering, June, 1997. (http://www.nps.edu/Faculty/Olsen/Student_theses/stefanou.pdf)

  • Identifying Roads And Trails Hidden Under Canopy Using Lidar

Fermin Espinoza-Lieutenant Commander, United States Navy Robb E. Owens-Major, United States Air Force September 2007 Advisor: Richard Christopher Olsen Second Reader: Mark C. Abrams (http://www.nps.edu/Faculty/Olsen/Student_theses/07Sep_Espinoza_Owens.pdf)

  • Depth Analysis Of Midway Atoll Using Quickbird Multi-Spectral Imaging Over Variable Substrates

Mark A. Camacho-Lieutenant, United States Naval Reserve September 2006 Advisor: Dr. Daria Siciliano Co-Advisor: Dr. Richard C. Olsen (http://www.nps.edu/Faculty/Olsen/Student_theses/06Sep_Camacho.pdf)