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Chapman, B.

Publications and source records attributed to Chapman, B..

32 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

NASA/JPL's Imaging Radar Outreach Program

In order to build a user community for future NASA imaging radar products and programs, outreach activities have been implemented by JPL. These include: education outreach, public awareness outreach, and outreach to areas of the scientific and applications community who are not traditional imaging radar users. A key component is the NASA/JPL Imaging Radar Home Page on the World Wide Web.

Radar Products Radar Programs Imaging Radar SIR-C ↗
NASA NTRS1996

The JERS-1 Amazon Multi-Season Mapping Study (JAMMS)

Regional mapping of the Amazon basin using imaging radar is described. Two 60-day periods of radar mapping will be conducted, one in 1995, and one in 1996. One period will view the low-water season, and the other will view during the high-flood season. The main objective of the JAMMS project is to generate a regional map showing inundation throughout the Amazon Basin by comparing the two data sets.

Amazon Radar Mapping Earth Satellite JAMMS flood p↗
NASA NTRS1995

Radar Images of the Earth and the World Wide Web

A perspective of NASA's Jet Propulsion Laboratory as a center of planetary exploration, and its involvement in studying the earth from space is given. Remote sensing, radar maps, land topography, snow cover properties, vegetation type, biomass content, moisture levels, and ocean data are items discussed related to earth orbiting satellite imaging radar. World Wide Web viewing of this content is discussed.

Radar Mapping↗
NASA NTRS1994

Amazon Rain Forest Classification Using J-ERS-1 SAR Data

The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.

J-ERS-1↗
NASA NTRS1993

The Brewster angle effect in SAR polarimetry

For the double bounce case, where the radar signal is reflected twice before returning to the radar antenna, some polarization effects may be observed related to the dielectric constant of the two surfaces causing the reflections. The most noticeable effect would be that the returned signal would be preferentially H polarized. In fact, it may be possible to discern the Brewster angle for both surfaces. The locations of the Brewster angle will depend on the dielectric constant and permittivity of each surface. If it is assumed that both reflections are in the same plane of incidence, and that both surfaces are smooth and flat, there is a straightforward relationship between the degree of linear polarization m and both the dielectric constants of the two reflecting surfaces and the angle of incidence of the illuminating wave: m carat = cos 2(arccot (square root of (R(sub v) / R(sub h)))) where R(sub v,h) are the V and H polarized Fresnel reflection coefficients for two surfaces perpendicular to each other. The degree of linear polarization may be calculated from AIRSAR compressed Stokes data and compared with the given equation. The degree of linear polarization may also be calculated using tree models and compared with AIRSAR data. With further work, it may be possible to use the degree of linear polarization to determine surface parameters of certain imaged areas.

Chapman, B.↗
NASA NTRS1992

A comparison of three SAR interference filters

Three interference filters are discussed and their ability to remove the interference while leaving target signal data is quantified. The filters are adaptive frequency-domain spike filtering; adaptive frequency-domain interference determination and masking; and interference-target correlation masking. Using Jet Propulsion Laboratory Airborne Synthetic Aperture Radar (JPL AIRSAR) data, the algorithms are applied to three data sets. The resultant compressed Stokes matrix data are compared with the normally processed interference-free image in a quantitative sense. The algorithms are evaluated by measuring the polarization signature, SNR, equivalent number of looks, and impulse response of the processed images.

Chapman, B.↗
NASA NTRS1992

JPL AIRSAR processing activities and developments

Significant progress has been made in processing the Jet Propulsion Laboratory airborne synthetic aperture radar (JPL AIRSAR) data. These advances include increased swath width, increased number of looks; increased processor throughput, and decreased processor turnaround time (including photo product). These advances are made possible by new processing algorithms, software, and hardware. In addition to these processor improvements, a more mature understanding of the AIRSAR system in general has made it possible for the processor to routinely produce calibrated data based on internal calibration tests. Starting with the processing of 1991 acquired data, this new processor has become operational for the routine processing of AIRSAR data.

Carande, R.↗