Stephen A. Mango

Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery

Polarimetric and interferometric SAR data are frequently multilook processed for speckle reduction and data compression. The statistical characteristics of multilook data are quite different from those of single-look data. The authors investigate the statistics of their intensity and phase. Probability density function (PDFs) of the multilook phase difference, magnitude of complex product, and intensity and amplitude ratios between two components of the scattering matrix are derived, and expressed in closed forms. The PDFs depend on the complex correlation coefficient and the number of looks. Comparisons of these theoretically derived PDFs are made to measurements from NASA/JPL AIRSAR data. The results of this paper can be applied to feature classification using polarimetric SAR and to the estimation of decorrelation effects of the interferometric SAR. >

Speckle reduction in multipolarization, multifrequency SAR imagery

An algorithm to take advantage of this polarization diversity to suppress the speckle effect with much less resolution broadening than using spatial filtering is discussed. The coupling between polarization channels is minimized by using local intensity ratios. The degree of speckle reduction is similar to two-look or three-look processing. The same algorithm can also be used to process multifrequency polarimetric SAR. For three-frequency aircraft SAR data speckle reduction equivalent to six-look processing can be achieved. Further speckle reduction is possible by applying speckle filters in the spatial domain. In addition, a vector speckle filter which operates simultaneously in the polarization and spatial domains is tested. Experimental results with simulated polarimetric SAR as well as one-look and multilook parametric SAR data demonstrate the effectiveness of these speckle reductions, with minimum resolution broadening and coupling between polarimetric and frequency channels. Comparisons with other algorithms are also made. >

Unsupervised estimation of speckle noise in radar images

Speckle in radar images has the characteristic of a multiplicative noise. In this article, two unsupervised methods are introduced to estimate the speckle noise statistics using the mean and the standard deviation of small image blocks (4 × 4, or 6 × 6 pixels). Since most radar images contain many small homogeneous areas, a scatter plot of the standard deviation versus the mean can reveal the characteristic of the noise. The blocks from inhomogeneous areas have higher values for the standard deviation, and they are scattered above the main cluster. They are considered as outliers, and should be excluded in the statistical estimation. These two methods are designed for obtaining a linear fit in the scatter plot by ignoring outliers. Several synthetic aperture radar(SAR) images are used for illustration.

Fuzzy classification of Earth terrain covers using multi-look polarimetric SAR image data

The conventional approach of terrain image classification which assigns a specific class for each pixel is inadequate because the area covered by each pixel may embrace more than a single class. Fuzzy set theory which has been developed to deal with imprecise information can provide a more appropriate solution to this problem. In the paper, the authors used the fuzzy c-means clustering algorithm for the segmentation of a polarimetric SAR image. The distance measure utilized in the algorithm was derived from the complex Wishart distribution of the pixel data presented in the covariance matrix format. The algorithm computes the feature covariance matrix for each class and generates a fuzzy partition of the whole image. Classification of the image is achieved using a defuzzification criterion. The results are similar to those of supervised statistical methods. NASA/JPL AIRSAR data is used to substantiate this fuzzy classification algorithm.<<ETX>>

Intensity and phase statistics of multi-look polarimetric SAR imagery

Polarimetric SAR data are frequently multi-look processed. The statistical characteristics of multi-look data are quite different from that of single-look data. In this paper, the authors investigate the distributions of intensity and phase. In particular, the probability density function (PDF) of the multi-look phase difference between co-polarized components is derived for the first time, and expressed in closed form. The PDF is found only depending on the complex correlation coefficient and the number of looks. Also included are PDFs of multi-look intensity and amplitude ratios. These PDFs are verified with NASA/JPL AIRSAR data. The results of this paper can also be applied to the interferometric radar for studying the decorrelation effects.<<ETX>>

Segmentation of SAR images using the wavelet transform

Multiresolution representation of images using the wavelet transform is a new approach for the analysis of image information content. The transform can be computed efficiently by a pyramidal algorithm based on convolution with quadrature mirror filters. The result is a set of subband images which consists of a lower resolution version of the original image and a sequence of detail images containing higher frequency spectral information. We used this representation for the supervised segmentation of polarimetric SAR images of the San Francisco Bay area acquired by the airborne JPL system for identifying various terrain covers. Since the wavelet transform generates the localized spatial and spectral information simultaneously, detailed knowledge of the texture variations within an image can be extracted from the data in the spectral subbands. The segmentation algorithm developed in this paper is formulated by taking into consideration both the intensity and the texture information. For polarimetric SAR images, the classification accuracy can be enhanced, if the combined data from copolarized and cross‐polarized images are used in the discrimination process. In contrast to other texture segmentation approaches, this algorithm does not require extensive calculations.©1993 John Wiley & Sons Inc

Study of Gulf Stream features with a multifrequency polarimetric SAR from the Space Shuttle

Using simulations of radar cross section (RCS) based on wave-current interaction calculations, the authors investigate the origin of a prominent enhancement in L-band from signals that were transmitted and received, respectively, with horizontal (H) and vertical (V) polarization radar return. This was observed in imagery of the northern boundary of the Gulf Stream (GS) during the first Shuttle Radar Laboratory (SRL-1) mission. The calculations of surface roughness are based on a one-dimensional (1D) surface current model that closely resembles a current shear that was observed in in situ current measurements, taken at both sides of the GS at the time SRL-1 imaged the GS boundary. In agreement with trends observed in the imagery, significant enhancements in L-band HV polarization cross section occur in the neighborhood of the GS thermal boundary, relative to comparable vertical polarization (VV) cross section signatures at X-, C-, and L-band. The authors also find reasonably good agreement between the simulated and observed magnitudes of the GS signatures (based on calculations of wave action) using two different radar imaging models, and they provide an overview of a number of additional submesoscale features associated with the GS that were present in the image of the GS boundary.

Study of Gulf Stream features with a multi-frequency polarimetric SAR from the space shuttle

The authors use simulations of radar cross-section, based on wave-current interaction calculations, to investigate the origin of a prominent enhancement in L-band, HV polarization radar return that was observed in imagery of the northern boundary of the Gulf Stream (GS) during the first Shuttle Radar Laboratory (SRL-1) mission. The calculations of surface roughness are based on a 1-dimensional surface current model that closely resembles a current convergence that was observed in in-situ current measurements, taken at both sides of the Stream at the time SRL-1 imaged the GS boundary. In agreement with trends observed in the imagery, significant enhancements in L-band HV polarization cross-section occur in the neighborhood of the GS boundary, relative to comparable VV polarization cross-section signatures at X-, C- and L-band. This occurs despite the fact that the magnitude of the L-band HV cross-section is significantly reduced relative to the comparable X-, C-, and L-band VV cross-sections. These results indicate that the associated L-band HV enhancement occurs from tilt-induced modulation in the radar backscatter, which preferentially alters the relative modulation in L-band HV backscatter in regions where considerable variation in surface slope takes place. The authors also provide an overview of a number of additional sub-mesoscale features associated with the Gulf Stream that were present in the image of the GS boundary.

Remote sensing of current-wave interaction with SIR-C/X-SAR (SRL-1) at the Gulf Stream supersite

The first mission of NASAs Shuttle Imaging Radar (SIR)-C/X-SAR (SRL-1) took place 9-20 April 1994. The authors report the activities and efforts for ground/sea truthing SRL-1 at the Gulf Stream (GS) supersite on current-wave interaction off the East Coast of the U.S. The resources used were the RN Cape Hatteras, the NRL/P-3 with RAR, the NAWC/P-3 with SAR, and the JPL/DC-8 AlRSAR. Other in-situ measurements within the GS supersite that will be used in the ensuing investigations are NOAA/NWS/NDBC off shore buoys and coastal stations. Also, in the comparisons and scientific investigations, AVHRR imagery of the supersite region have been obtained, and SAR images from four passess of ESAs ERS-1 satellite over the GS supersite during this time frame will be available. Preliminary results of the comparisons and investigations are described.<<ETX>>

Texture Segmentation of Sar Images Using the Wavelet Transform

Multiresolution representation of an image using the wavelet transform is a new and effective approach for the analysis of image information content. The transform can be computed efficiently by a pyramidal algorithm based on convolutions with quadrature mirror filters. The result is a set of sub-band images which consists of a lower resolution version of the original image and a sequence of detail images containing higher spectral information. We used this representation for supervised texture segmentation of polarimetric SAR images acquired by the airborne JPL system. Since the transform generates localized spatial and spectral information simultaneously, texture segmentation can be accomplished by examining the spatial variations of the spectral sub-bands.