Single-polarization Synthetic Aperture Radar Research Articles

Texture analysis and classification of ERS SAR images for map updating of urban areas in the netherlands

In single-band and single-polarized synthetic aperture radar (SAR) image classification, texture holds useful information. In a study to assess the map-updating capabilities of such sensors in urban areas, some modern texture measures were investigated. Among them were histogram measures, wavelet energy, fractal dimension, lacunarity, and semivariograms. The latter were chosen as an alternative for the well-known gray-level cooccurrence family of features. The area that was studied using a European Remote Sensing Satellite 1 (ERS-1) SAR image was the conurbation around Rotterdam and The Hague in The Netherlands. The area can be characterized as a well-planned dispersed urban area with residential areas, industry, greenhouses, pasture, arable land, and some forest. The digital map to be updated was a 1:250000 Vector Map (VMap1). The study was done on the basis of nonparametric separability measures and classification techniques because most texture distributions were not normal. The conclusion is that texture improves the classification accuracy. The measures that performed best were mean intensity (actually no texture), variance, weighted-rank fill ratio, and semivariogram, but the accuracies vary for different classes. Despite the improvement, the overall classification accuracy indicates that the land-cover information content of ERS-1 leaves something to be desired.

Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR

Addresses the land-use classification capabilities of fully polarimetric synthetic aperture radar (SAR) versus dual-polarization and single-polarization SAR for P-, L-, and C-band frequencies. A variety of polarization combinations are investigated for application to crop and tree age classification. Based on the complex Wishart distribution for the covariance matrix, maximum likelihood classifiers for all polarization combinations are used to assess quantitative classification accuracy. Thus, this allows optimally selecting the frequency and the combination of polarizations for various applications.

Measurements of snow- and glacier-covered areas with single-polarization SAR

Climatological and hydrological investigations in mountainous regions frequently require delineation of the areas covered by snow and glacial ice. Active microwave sensors can discriminate snow and glacier from other targets, are effective under all weather conditions, and have a spatial resolution compatible with the topographic variation in alpine regions. Using data acquired with the NASA AIRSAR, which operates at three frequencies and four combinations of polarization, we examine the usage of synthetic aperture radar (SAR) to map snow- and glacier-covered areas. In order to assess the available SARs that operate from satellites, we analyze single-frequency, single-polarization data, and we compare our results with images from the Landsat Thematic Mapper obtained under clear conditions only a few days after the SAR flight. Single polarization, C-band (5.3 GHz) SAR data can discriminate between areas covered by wet snow from those that are ice-free, but do not easily separate glacier ice from snow and rock. L-band SAR (1.25 GHz) data cannot discriminate between snow and glacier ice. The accuracy (74%) of SAR is high enough to justify its use as the data source in areas that are too cloud-covered to obtain reliable data from the thematic mapper.

Measurements of snow- and glacier-covered areas with single-polarization SAR

Climatological and hydrological investigations in mountainous regions frequently require delineation of the areas covered by snow and glacial ice. Active microwave sensors can discriminate snow and glacier from other targets, are effective under all weather conditions, and have a spatial resolution compatible with the topographic variation in alpine regions. Using data acquired with the NASA AIRSAR, which operates at three frequencies and four combinations of polarization, we examine the usage of synthetic aperture radar (SAR) to map snow- and glacier-covered areas. In order to assess the available SARs that operate from satellites, we analyze single-frequency, single-polarization data, and we compare our results with images from the Landsat Thematic Mapper obtained under clear conditions only a few days after the SAR flight. Single polarization, C-band (5.3 GHz) SAR data can discriminate between areas covered by wet snow from those that are ice-free, but do not easily separate glacier ice from snow and rock. L-band SAR (1.25 GHz) data cannot discriminate between snow and glacier ice. The accuracy (74%) of SAR is high enough to justify its use as the data source in areas that are too cloud-covered to obtain reliable data from the thematic mapper.

Characterization of spatial statistics of distributed targets in SAR data

Abstract A method for the analysis of spatial statistics in multifrequency polarimetric Synthetic Aperture Radar (SAR) data is presented. The objective is to extract the intrinsic variability of the target by removing the variability from other sources. Three sources which contribute to the spatial variability in the returned power from a distributed target are modelled, they are (1) image speckle, (2) system noise, and (3) the intrinsic spatial variability of the target or texture. Speckle and system noise are modelled based on an understanding of the physics of the SAR imaging and processing systems. Texture is modelled as a random variable which modulates the mean returned power from a distributed target. An image model which accounts for all three sources of variability is presented. The presence of texture is shown to increase the image variance-to-mean square ratio and to introduce deviations of the image autocovariance function from the expected SAR system response. Two textural parameters, the standard deviation of texture and its autocovariance coefficient, are examined. This statistical approach is illustrated using sea-ice SAR imagery acquired by the Jet Propulsion Laboratory three-frequency polarimetric airborne SAR. Textural modulation of the signal has been detected in the imagery. Results show that for different sea-ice types the spatial statistics seem to vary more across frequency than across polarization and the observed differences increase in magnitude with decreasing frequency. The results also suggest the potential of this approach for discrimination of various sea-ice types and open water in single frequency, single polarization SAR data. Correlation of the spatial statistics to field measurements will be important for the verification of these observations.