Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

/pdf/synthetic-aperture-radar-interferometry-28lm7jf0if.pdf

Synthetic aperture radar interferometry

Remote sensing from airborne and spaceborne platforms provides valuable data for mapping, environmental monitoring, disaster management and civil and military intelligence. However, to explore the full value of these data, the appropriate information has to be extracted and presented in standard format to import it into geo-information systems and thus allow efficient decision processes. The object-oriented approach can contribute to powerful automatic and semi-automatic analysis for most remote sensing applications. Synergetic use to pixel-based or statistical signal processing methods explores the rich information contents. Here, we explain principal strategies of object-oriented analysis, discuss how the combination with fuzzy methods allows implementing expert knowledge and describe a representative example for the proposed workflow from remote sensing imagery to GIS. The strategies are demonstrated using the first object-oriented image analysis software on the market, eCognition, which provides an appropriate link between remote sensing imagery and GIS.

Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

: The classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms, including those based on the sum of squares criterion and on the criterion of Friedman and Rubin (1967). However, as currently implemented, it does not allow the specification of which features (orientation, size and shape) are to be common to all clusters and which may differ between clusters. Also, it is restricted to Gaussian distributions and it does not allow for noise. We propose ways of overcoming these limitations. A reparameterization of the covariance matrix allows us to specify that some features, but not all, be the same for all clusters. A practical framework for non-Gaussian clustering is outlined, and a means of incorporating noise in the form of a Poisson process is described. An approximate Bayesian method for choosing the number of clusters is given. The performance of the proposed methods is studied by simulation, with encouraging results. The methods are applied to the analysis of a data set arising in the study of diabetes, and the results seem better than those of previous analyses. (RH)

Model-based Gaussian and non-Gaussian clustering

Geophysical applications of radar interferometry to measure changes in the Earth's surface have exploded in the early 1990s. This new geodetic technique calculates the interference pattern caused by the difference in phase between two images acquired by a spaceborne synthetic aperture radar at two distinct times. The resulting interferogram is a contour map of the change in distance between the ground and the radar instrument. These maps provide an unsurpassed spatial sampling density (∼100 pixels km−2), a competitive precision (∼1 cm), and a useful observation cadence (1 pass month−1). They record movements in the crust, perturbations in the atmosphere, dielectric modifications in the soil, and relief in the topography. They are also sensitive to technical effects, such as relative variations in the radar's trajectory or variations in its frequency standard. We describe how all these phenomena contribute to an interferogram. Then a practical summary explains the techniques for calculating and manipulating interferograms from various radar instruments, including the four satellites currently in orbit: ERS-1, ERS-2, JERS-1, and RADARSAT. The next chapter suggests some guidelines for interpreting an interferogram as a geophysical measurement: respecting the limits of the technique, assessing its uncertainty, recognizing artifacts, and discriminating different types of signal. We then review the geophysical applications published to date, most of which study deformation related to earthquakes, volcanoes, and glaciers using ERS-1 data. We also show examples of monitoring natural hazards and environmental alterations related to landslides, subsidence, and agriculture. In addition, we consider subtler geophysical signals such as postseismic relaxation, tidal loading of coastal areas, and interseismic strain accumulation. We conclude with our perspectives on the future of radar interferometry. The objective of the review is for the reader to develop the physical understanding necessary to calculate an interferogram and the geophysical intuition necessary to interpret it.

/pdf/radar-interferometry-and-its-application-to-changes-in-the-2xi2itqotn.pdf

Radar interferometry and its application to changes in the Earth's surface

Synthetic aperture radar interferometry (InSAR) from Earth-orbiting spacecraft provides a new tool to map global topography and deformation of the Earth's surface. Radar images taken from slightly different viewing directions allow the construction of digital elevation models of meter-scale accuracy. These data sets aid in the analysis and interpretation of tectonic and volcanic landscapes. If the Earth's surface deformed between two radar image acquisitions, a map of the surface dis- placement with tens-of-meters resolution and subcentimeter accuracy can be con- structed. This review gives a basic overview of InSAR for Earth scientists and presents a selection of geologic applications that demonstrate the unique capabilities of InSAR for mapping the topography and deformation of the Earth.

/pdf/synthetic-aperture-radar-interferometry-to-measure-earth-s-1nugx1vz24.pdf

Synthetic aperture radar interferometry to measure earth's surface topography and its deformation

The principles governing synthetic aperture radar (SAR) and its use on the Seasat spacecraft are reviewed. The way in which wind stress, surface currents, long gravity waves, and surface films modulate the scattering properties of resonant (approximately 30-cm-wavelength) waves is discussed, with particular emphasis placed on the mechanisms that could produce images of long gravity waves. Doppler effects by ocean motion are also described. Measurements of long (wavelength more than about 100 m) gravity waves made using Seasat SAR imagery are compared with surface measurements during several experiments. Combining these results, it is found that dominant wavelength and direction are measured by Seasat SAR within + or - 12% and + or - 15 deg, respectively. It is noted, however, that ocean waves are not always visible in SAR images, and detection criteria are discussed in terms of wave height, length, and direction.

The observation of ocean surface phenomena using imagery from the SEASAT synthetic aperture radar: An assessment

It is known that interferometric synthetic-aperture radar (SAR) images can be inverted to perform surface elevation mapping. Among the factors critical to the mapping accuracy are registration of the interfering SAR images and phase unwrapping. A registration algorithm is presented that determines the registration parameters through optimization. A figure of merit is proposed that evaluates the registration result during the optimization. The phase unwrapping problem is approached through a new method involving fringe line detection. The algorithms are tested with two SEASAT SAR images of terrain near Yellowstone National Park. These images were collected on SEASAT orbits 1334 and 1420, which were very close together in space, i.e. less than 100 m. >

New approaches in interferometric SAR data processing

A high-frequency multifrequency coastal radar operating at four frequencies between 4.8 and 21.8 MHz was used as part of the third Chesapeake Bay Outflow Plume Experiment (COPE-3) during October and November, 1997. The radar system surveyed the open ocean east of the coast and just south of the mouth of Chesapeake Bay from two sites separated by about 20 km. Measurements were taken once an hour, and the eastward and northward components of ocean currents were estimated at four depths ranging from about 0.5 m to 2.5 m below the surface for each location on a 2 by 2 km grid. Direction of arrival of the signals was estimated using the MUSIC algorithm. The radar measurements were compared to currents measured by several moored acoustic Doppler current profilers (ADCPs) with range bins 2-14 m below the water surface. The vertical structure of the current was examined by utilizing four different radar wavelengths, which respond to ocean currents at different depths, and by using several ADCP range bins separated by 1-m intervals. The radar and ADCP current estimates were highly correlated and showed similar depth behavior, and there was significant correlation between radar current estimates at different wavelengths and wind speed.

A comparison of multifrequency HF radar and ADCP measurements of near-surface currents during COPE-3

Two techniques for automated sea-ice tracking, image pyramid area correlation (hierarchical correlation) and feature tracking, are described. Each technique is applied to a pair of Seasat SAR sea-ice images. The results compare well with each other and with manually tracked estimates of the ice velocity. The advantages and disadvantages of these automated methods are pointed out. Using these ice velocity field estimates it is possible to construct one sea-ice image from the other member of the pair. Comparing the reconstructed image with the observed image, errors in the estimated velocity field can be recognized and a useful probable error display created automatically to accompany ice velocity estimates. It is suggested that this error display may be useful in segmenting the sea ice observed into regions that move as rigid plates of significant ice velocity shear and distortion. >

Observation of sea-ice dynamics using synthetic aperture radar images: automated analysis

Estimates of the variation with depth of both the speed and direction of near-surface ocean currents within the top meter have been made using time series data collected from a dual-frequency, high-frequency radar located on the California coast, south of Monterey. Long-term averages derived from this data set over 3 different years consistently revealed an expected reduction in the speed of the near-surface current as a function of depth, consistent with a logarithmic vertical current profile. An unexpected and previously unmeasured clockwise rotation with increasing depth of the near-surface current was also observed. Hypotheses are suggested for the apparent rotation.

John F. Vesecky

Papers

The observation of ocean surface phenomena using imagery from the SEASAT synthetic aperture radar: An assessment

New approaches in interferometric SAR data processing

A comparison of multifrequency HF radar and ADCP measurements of near-surface currents during COPE-3

Observation of sea-ice dynamics using synthetic aperture radar images: automated analysis

Measurements of upper ocean surface current shear with high-frequency radar