Image-processing techniques for extracting the characteristics of lead and pressure ridge features in SAR images of sea ice are reported. The methods are applied to a SAR image of the Beaufort Sea collected from the Seasat satellite on October 3, 1978. Estimates of lead and ridge statistics are made, e.g., lead and ridge density (number of lead or ridge pixels per unit area of image) and the distribution of lead area and orientation as well as ridge length and orientation. The information derived is useful in both ice science and polar operations for such applications as albedo and heat and momentum transfer estimates, as well as ship routing and offshore engineering.
Sea ice ridges and keels (hummocks and bummocks) are important in sea ice research for both scientific and practical reasons. A long-term objective is to make quantitative measurements of sea ice ridges using synthetic aperture radar (SAR) images. The preliminary results of a scattering model for sea ice ridge are reported. The approach is through the ridge height variance spectrum Psi(K), where K is the spatial wavenumber, and the two-scale scattering model. The height spectrum model is constructed to mimic height statistics observed with an airborne optical laser. The spectrum model is used to drive a two-scale scattering model. Model results for ridges observed at C- and X-band yield normalized radar cross sections that are 10 to 15 dB larger than the observed cross sections of multiyear ice over the range of angles of incidence from 10 to 70 deg.
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.< >
The authors describe an algorithm for estimating the proportions of classes in a SAR (synthetic aperture radar) image without any user interaction. The method assumes that the image is a mixture of a known number of different pixel types. A maximum likelihood estimate of the parameters of the resulting mixture distribution is then used to find the proportions for the various classes. The technique was successfully applied to aircraft SAR images of sea ice. Computer simulations were used to determine the relative errors of the technique. The technique performs well even with extremely noisy images.
An automated algorithm for finding the inner boundary based on recently proposed computer vision technique is described. The algorithm is analogous to solving the equations of motion for an elastic curve, where the forces are provided by the image. The resulting equilibrium position of the elastic curve provides an automated method for finding the shape and location of the inner boundary of the auroral oval. Two methods for the evaluation of the automated algorithm, both based on the comparisons with manual measurements, are developed. The first method compares the areas within the automated and the manual boundaries. The second method measures the overlap between the interiors of the two boundaries. The expected variation between two sets of manual measurements is used to set an upper bound to the allowed discrepancy between the automated results and a single set of manual measurements. The algorithm, when tested with 71 satellite images, is found to perform best for those images without overlap between the aurora and the dayside hemisphere.
An unsupervised method that chooses and applies the most appropriate tracking algorithm from among different sea-ice tracking algorithms is reported. In contrast to current unsupervised methods, this method chooses and applies an algorithm by partially examining a sequential image pair to draw inferences about what was examined. Based on these inferences the reported method subsequently chooses which algorithm to apply to specific areas of the image pair where that algorithm should work best.
The DE-1 satellite has gathered over 500,000 images of the Earth's aurora. Finding the location and shape of the boundaries of the oval is of interest to geophysicists but manual extraction of the boundaries is extremely time consuming. This paper describes a computer vision system that automatically provides an estimate of the inner auroral boundary for winter hemisphere scenes. The system performs automatic checks of its boundary estimate. If the boundary estimate is deemed inconsistent, the system does not output it. The performance of this system is evaluated using 44 DE-1 images. The system provides boundary estimates for 37 of the inputs. Of these 37 estimates, 31 are consistent with the corresponding manual estimates. At this level of performance, the supervised use of the system provides more than one order of magnitude increase in throughput compared to manual extraction of the boundaries.
The ESA's ERS-1 satellite will carry SARs over the polar regions; an important component in the use of these data is an automated scheme for the extraction of sea ice velocity fields from a sequence of SAR images of the same geographical region. The image pyramid area-correlation hierarchical method is noted to be vulnerable to uncertainties for sea ice rotations greater than 10-15 deg between SAR observations. Rotation-invariant methods can successfully track isolated floes in the marginal ice zone. Hu's (1962) invariant moments are also worth considering as a possible basis for rotation-invariant tracking methods. Feature tracking is inherently robust for tracking rotating sea ice, but is limited when features are floe-lead boundaries. A variety of techniques appears neccessary.
A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice.
