Showing papers in "IEEE Transactions on Geoscience and Remote Sensing in 1996"
TL;DR: This paper unify the formulation of these different approaches using transformation theory and an eigenvector analysis of the covariance or coherency matrix of the scattering matrix for target decomposition theory in radar polarimetry.
Abstract: In this paper, we provide a review of the different approaches used for target decomposition theory in radar polarimetry. We classify three main types of theorem; those based on the Mueller matrix and Stokes vector, those using an eigenvector analysis of the covariance or coherency matrix, and those employing coherent decomposition of the scattering matrix. We unify the formulation of these different approaches using transformation theory and an eigenvector analysis. We show how special forms of these decompositions apply for the important case of backscatter from terrain with generic symmetries.
2,369 citations
TL;DR: A generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data is proposed, which is less sensitive to uncertainty in emissivity and to instrument quantization error, and retrieves land- surface temperature more accurately.
Abstract: Proposes a generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must vary with the viewing angle, if the authors are to achieve a LST accuracy of about 1 K for the whole scan swath range (/spl plusmn/55/spl deg/ from nadir) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. The authors obtain these coefficients from regression analysis of radiative transfer simulations, and they analyze sensitivity and error over wide ranges of surface temperature and emissivity and atmospheric water vapor abundance and temperature. Simulations show that when atmospheric water vapor increases and viewing angle is larger than 45/spl deg/, it is necessary to optimize the split-window method by separating the ranges of the atmospheric water vapor, lower boundary temperature, and the surface temperature into tractable subranges. The atmospheric lower boundary temperature and (vertical) column water vapor values retrieved from HIRS/2 or MODIS atmospheric sounding channels can be used to determine the range for the optimum coefficients of the split-window method. This new algorithm not only retrieves land-surface temperature more accurately, but is also less sensitive to uncertainty in emissivity and to instrument quantization error.
1,553 citations
TL;DR: In this article, a Bayesian approach was proposed for retrieving the precipitation and vertical hydrometeor profiles from downward viewing radiometers based on a prior probability density function of rainfall profiles, which is computationally much less expensive than previous profiling schemes and has been designed specifically to allow for tractability of assumptions.
Abstract: Presents a computationally simple technique for retrieving the precipitation and vertical hydrometeor profiles from downward viewing radiometers. The technique is computationally much less expensive than previous profiling schemes and has been designed specifically to allow for tractability of assumptions. In this paper, the emphasis is placed upon passive microwave applications, but the combination of passive with active microwave sensors, infrared sensors, or other a priori information can be adapted easily to the framework described. The technique is based upon a Bayesian approach. The authors use many realizations of the Goddard Cumulus Ensemble model to establish a prior probability density function of rainfall profiles. Detailed three-dimensional radiative transfer calculations are used to determine the upwelling brightness temperatures from the cloud model to establish the similarity of radiative signatures and thus the probability that a given profile is actually observed. In this study, the authors show that good results may be obtained by weighting profiles from the prior probability density function according to their deviation from the observed brightness temperatures. Examples of the retrieval results are shown for oceanic as well as land situations. Microwave data from the Advanced Microwave Precipitation Radiometer (AMPR) instrument are used to illustrate the retrieval structure results for high-resolution data while SSM/I is used to illustrate satellite applications. Simulations are performed to compare the expected retrieval performance of the SSM/I instrument with that of the upcoming TMI instrument aboard the Tropical Rainfall Measuring Mission (TRMM) to be launched in August 1997. These simulations show that correlations of /spl sim/0.77 may be obtained for 10-km retrievals of the integrated liquid water content based upon SSM/I channels. This correlation increases to /spl sim/0.90 for the same retrievals using the TMI channels and resolution. Due to the lack of quantitative validation data, hydrometeor profiles cannot be compared directly but are instead converted to an equivalent reflectivity structure and compared to existing radar observations where possible.
484 citations
TL;DR: In this article, a generalized formulation of the extended chirp scaling (ECS) approach for high precision processing of air- and spaceborne SAR data is presented, which allows an effective phase-preserving processing of ScanSAR data without interpolation for azimuth geometric correction.
Abstract: Presents a generalized formulation of the extended chirp scaling (ECS) approach for high precision processing of air- and spaceborne SAR data. Based on the original chirp scaling function, the ECS algorithm incorporates a new azimuth scaling function and a subaperture approach, which allow an effective phase-preserving processing of ScanSAR data without interpolation for azimuth geometric correction. The azimuth scaling can also be used for automatic azimuth coregistration of interferometric image pairs which are acquired with different sampling distances. Additionally, a novel range scaling formulation is proposed for automatic range coregistration of interferometric image pairs or for improved robustness for the processing of highly squinted data. Several simulation and processing results of air- and spaceborne SAR data are presented to demonstrate the validity of the proposed algorithms.
475 citations
TL;DR: The authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery.
Abstract: A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery.
447 citations
TL;DR: Taking into account the extremely strong variations in sea state in some of the selected scenes, the automatic detection performance is considered to be very good.
Abstract: An automatic ship and ship wake detection system for spaceborne SAR images is described and assessed The system is designed for coastal regions with eddies, fronts, waves and swells The system uses digital terrain models to simulate synthetic SAR images to mask out land areas Then a search for ship targets is performed followed by wake search around detected ship candidates Finally, a homogeneity test and wake behavior test are performed which reduces the number of false alarms substantially The system is demonstrated with ERS-1 SAR images and its performance is assessed using Seasat and ERS-1 images No other information about the ships was available, hence, the basis for the assessment is through comparison with human visual interpretation of the same data The number of lost ships (ship-like targets) was 7-8% for both Seasat-A and ERS-1 No false ships were detected The number of lost or false wakes (wake-like features) was higher in ERS-1 images than in Seasat-A images and was nearly 15% Taking into account the extremely strong variations in sea state in some of the selected scenes, the automatic detection performance is considered to be very good In addition, the requirement of analyzing a 3-look ERS-1 scene of 100 km/spl times/100 km in less than eight minutes has been achieved on a workstation
350 citations
TL;DR: A model for light interaction with forest canopies is presented, based on Monte Carlo simulation of photon transport, which shows close agreement between model predictions and field measurements of bidirectional reflectance, high-resolution spectra and hemispherical albedo.
Abstract: A model for light interaction with forest canopies is presented, based on Monte Carlo simulation of photon transport. A hybrid representation is used to model the discontinuous nature of the forest canopy. Large scale structure is represented by geometric primitives defining shapes and positions of the tree crowns and trunks. Foliage is represented within crowns by volume-averaged parameters describing the structural and optical properties of the scattering elements. Simulation of three-dimensional photon trajectories allows accurate evaluation of multiple scattering within crowns, and between distinct crowns, trunks and the ground surface. The sky radiance field is treated as anisotropic and decoupled from bidirectional reflectance calculation. Validation has been performed on an example of dense spruce forest. Results show close agreement between model predictions and field measurements of bidirectional reflectance, high-resolution spectra and hemispherical albedo.
308 citations
TL;DR: Polarimetric signatures of two primitives shapes (dihedral and edge) are simulated using high-frequency electromagnetic scattering methods and shown that the polarimetric responses of the primitive shapes are remarkably stable as the shapes are rotated about various axes.
Abstract: Polarimetric signatures of two primitives shapes (dihedral and edge) are simulated using high-frequency electromagnetic scattering methods. Signatures are predicted for a variety of orientations of the shapes. Polarimetric responses are analyzed using a polarization scattering matrix decomposition developed by Cameron and Leung (1990, 1992). It is shown that symmetric scatterer responses can be represented as points contained in the unit disc of the complex plane. The simulation shows that the polarimetric responses of the primitive shapes are remarkably stable as the shapes are rotated about various axes.
279 citations
TL;DR: The relative dielectric constant, or relative permittivity, of dry snow, is independent of frequency from about 1 MHz up to the microwave range of at least 10 GHz, and the data indicate that /spl epsiv/ is a function of snow density only.
Abstract: The relative dielectric constant, or relative permittivity, /spl epsiv/ of dry snow, is independent of frequency from about 1 MHz up to the microwave range of at least 10 GHz. New measurements of with improved accuracy were made with a specially designed resonator operating near 1 GHz. The coaxial sensor accurately defines the sample volume whose actual mass can be determined to give the density of the snow sample. A special electronic instrument, called a resometer, enabled accurate and rapid measurements under field conditions. Some 90 measurements of different kinds of dry snow (fresh, old, wind-pressed snow, depth hear, and refrozen crusts) were made at test sites in the Swiss and Austrian Alps. The data indicate that /spl epsiv/ is a function of snow density only, given that the standard deviation of 0.006 from the fitted curve is just due to the expected measurement errors. The interpretation of these data in terms of physical mixing theory favors the effective medium formula of Polder and van Santen (1946). The data allow to relate the average axial ratio X as a function of ice volume fraction. Both prolate and oblate spheroids can explain the data. Independent reasoning gives preference to oblate particles. In both cases, the axial ratio increases with increasing fraction up to a critical value of 0.33, followed by a decrease at still higher fractions. The destructive metamorphism of slowly compacting snow explains the increase of X, while the following decrease might be due to sintering. So far, no effect on /spl epsiv/ by a liquid-like surface layer on the ice grains at temperatures between -10/spl deg/C and 0/spl deg/C has been observed.
275 citations
TL;DR: In this article, a rational approach to the design of an optimal index to estimate vegetation properties on the basis of the red and near-infrared reflectances of the AVHRR instrument, taking into account the perturbing effects of soil brightness changes, atmospheric absorption and scattering.
Abstract: Satellite remote sensing data constitute a significant potential source of information on our environment, provided they can be adequately interpreted. Vegetation indexes, a subset of the class of spectral indexes, represent one of the most commonly used approaches to analyze data in the optical domain. An optimal spectral index is very sensitive to the desired information (e.g. the amount of vegetation), and as insensitive as possible to perturbing factors (such as soil color changes or atmospheric effects). Since both the desired signal and the perturbing factors vary spectrally, and since the instruments themselves only provide data for particular spectral bands, optimal indexes should be designed for specific applications and particular instruments. This paper describes a rational approach to the design of an optimal index to estimate vegetation properties on the basis of the red and near-infrared reflectances of the AVHRR instrument, taking into account the perturbing effects of soil brightness changes, atmospheric absorption and scattering. The rationale behind the Global Environment Monitoring index (GEMI) is explained, and this index is proposed as an alternative to the Normalized Difference Vegetation Index (NDVI) for global applications. The techniques described here are generally applicable to any multispectral sensor and application.
263 citations
TL;DR: Comparisons are made between the SSM/I snowcover product and the NOAA/NESDIS subjectively analyzed weekly product, and an objective algorithm to monitor the global distribution of snowcover is developed.
Abstract: Visible satellite sensors have monitored snowcover throughout the Northern Hemisphere for almost thirty years. These sensors can detect snowcover during daylight, cloud-free conditions. The operational procedure developed by NOAA/NESDIS requires an analyst to manually view the images in order to subjectively distinguish between clouds and snowcover. Because this procedure is manually intensive, it is only performed weekly. Since microwave sensors see through nonprecipitating clouds, snowcover can be determined objectively without the intervention of an analyst. Furthermore, microwave sensors can provide daily analysis of snowcover in real-time, which is essential for operational forecast models and regional hydrologic monitoring. Snowcover measurements are obtained from the Special Sensor Microwave Imager (SSM/I), flown aboard the DMSP satellites. A decision tree, containing various filters, is used to separate the scattering signature of snowcover from other scattering signatures. Problem areas are discussed and when possible, a filter is developed to eliminate biases. The finalized decision tree is an objective algorithm to monitor the global distribution of snowcover. Comparisons are made between the SSM/I snowcover product and the NOAA/NESDIS subjectively analyzed weekly product.
TL;DR: This paper presents a new method for weighted least squares phase unwrapping that is a multigrid technique that solves the equations on smaller, coarser grids by means of Gauss-Seidel relaxation schemes and transfers the intermediate results to the finer grids.
Abstract: Weighted least squares phase unwrapping is a robust approach to phase unwrapping that unwraps around (rather than through) regions of corrupted phase. Currently, the only practical method for solving the weighted least squares equations is a preconditioned conjugate gradient (PCG) technique. In this paper the authors present a new method for weighted least squares phase unwrapping. Their method is a multigrid technique that solves the equations on smaller, coarser grids by means of Gauss-Seidel relaxation schemes and transfers the intermediate results to the finer grids. A key idea of their approach is to maintain the partial derivatives of the given phase data in separate arrays and to correct these derivatives at the boundaries of the coarser grids. This correction maintains the boundary conditions necessary for convergence to the correct solution. Another key idea of their approach is to transfer the weighting values to the coarser grids in a carefully defined manner. They also present methods for defining the initial phase weights in an automated fashion. The resulting multigrid algorithm converges in only one or two multigrid cycles and is generally 15-25 times faster than the PCG technique.
TL;DR: The results of the study indicate that the artificial neural network (ANN) estimates conifer mortality more accurately than the other approaches and offers a viable alternative for change detection in remote sensing.
Abstract: A prolonged drought in the Lake Tahoe Basin in California has resulted in extensive conifer mortality. This phenomenon can be analyzed using (multitemporal) remote sensing data. Prior research in the same region used more traditional methods of change detection. The present paper introduces a third approach to change detection in remote sensing based on artificial neural networks. The neural network architecture used is a multilayer feedforward network. The results of the study indicate that the artificial neural network (ANN) estimates conifer mortality more accurately than the other approaches. Further, an analysis of its architecture reveals that it uses identifiable scene characteristics-the same as those used by a Gramm-Schmidt transformation. ANN models offer a viable alternative for change detection in remote sensing.
TL;DR: A fully three-dimensional, finite-difference time-domain (FDTD) model of a ground-penetrating radar is described and results of scattering from three different buried cylindrical pipes are shown to be in good agreement.
Abstract: A fully three-dimensional, finite-difference time-domain (FDTD) model of a ground-penetrating radar is described. The FDTD simulation completely models the transmitting and receiving antennas, the antenna feeds, the dispersive Earth, and the buried object. Results of scattering from three different buried cylindrical pipes are compared to previously measured results for a one-third size scale model of an actual radar and are shown to be in good agreement.
TL;DR: A model, based on the radiative transfer theory and the matrix doubling algorithm, is described and used to compute the emissivity e of forests and it is found that the L-band emISSivity trend versus forest biomass is more gradual than that of the backscatter coefficient.
Abstract: A model, based on the radiative transfer theory and the matrix doubling algorithm, is described and used to compute the emissivity e of forests. According to model simulations, the L-band emissivity trend versus forest biomass is more gradual than that of the backscatter coefficient. This gradual behavior is observed, in absence of leaves, also at C- and X-bands, while leaves anticipate saturation and make e higher in coniferous forests and lower in deciduous forests. Model results are successfully validated by some available experimental data. Operational aspects, concerning the potential of airborne and spaceborne radiometers in identifying forest type and estimating biomass, are discussed.
TL;DR: An algorithm is described to obtain the slope correction from a SAR interferogram, which also enables retrieval of the full scattering geometry, and demonstrates that the spatial resolution and calibration error are adequate for most applications.
Abstract: The brightness in a SAR image is affected by topographic height variations due to (1) the projection between ground and image coordinates, and (2) variations in backscattering coefficient with the local scattering geometry. This paper derives a new equation for (1), i.e. the radiometric slope correction, based on a calibration equation which is invariant under a coordinate transformation. An algorithm is described to obtain the slope correction from a SAR interferogram, which also enables retrieval of the full scattering geometry. Since the SAR image and interferogram are derived from the same data set, there is no need to match the image with the calibration data. There is also no need for phase unwrapping since the algorithm only uses the fringe frequencies. A maximum-likelihood estimator for the fringe frequency is analyzed and the algorithm is illustrated by processing ERS-1 SAR data. The example demonstrates that the spatial resolution and calibration error are adequate for most applications.
TL;DR: It is demonstrated that the topography is separable from the surface displacement field when a sequence of radar images are available and a pure displacement field can be obtained by removal of the topographic contribution to the interferometric phase at each pixel.
Abstract: Both topography and motion information are present in repeat pass ERS-1 interferograms over ice sheets. The authors demonstrate that the topography is separable from the surface displacement field when a sequence of radar images are available. If the velocity field is constant over the time span of observation, the topography can be derived from differential interferograms formed from sequential observations. With this measurement, a pure displacement field can then be obtained by removal of the topographic contribution to the interferometric phase at each pixel. Further, they discuss how the vertical and horizontal components of displacement affect the interferometrically-derived motion field. They illustrate their approach with four successive (3-day repeat) ERS-1 images of a flow feature in northeastern Greenland.
TL;DR: Comparisons of the present with previous results suggest that the background (understory and ground cover) signal and the tree crown shadows are important in satellite measurements of FPAR.
Abstract: Measurements of the fraction of photosynthetically active radiation (FPAR) absorbed by the forest overstory were made at 20 sites in black spruce (Picea mariana) and jack pine (Pinus banksiana) boreal forests located in Saskatchewan and Manitoba, Canada. Canopies of both species have similar vertical tree crown structure but different branch and shoot architecture. Intensive investigation was made on the effect of these canopy architecture on the penetration of total visible radiation into the canopy at various solar zenith angles /spl theta/, quantified using the projection coefficient G/sub t/(/spl theta/). Based on experimental evidence, constant values of G/sub t/(/spl theta/) and the above- and below-canopy PAR reflectivities are suggested for these two species for the calculation of daily green FPAR. The calculation then requires only a single stand parameter: the effective green leaf area index (LAI) L/sub eg/, which is similar to the effective LAI L/sub e/ measured using optical instruments but reduced by a small fraction to remove the contribution of woody material to the total above-ground plant area. Daily green FPAR of the sites was correlated with the Simple Ratio (SR) and the Normalized Difference Vegetation Index (NDVI) obtained from Landsat 5 TM images. The correlation was better in late-spring than in mid-summer, suggesting spring images are more useful for obtaining FPAR of the overstory. Comparisons of the present with previous results suggest that the background (understory and ground cover) signal and the tree crown shadows are important in satellite measurements of FPAR.
TL;DR: This method efficiently reduces the data volume while retaining highly acceptable classification accuracy and the overall accuracy can be as high as 95% with a total of thirteen cover types classified.
Abstract: A practical method for extracting microwave backscatter for terrain-cover classification is presented The test data are multifrequency (P, L, C bands) polarimetric SAR data acquired by JPL over an agricultural area called "Flevoland" The terrain covers include forest, water, bare soil, grass, and eight other types of crops The radar response of crop types to frequency and polarization states were analyzed for classification based on three configurations: 1) multifrequency and single-polarization images; 2) single-frequency and multipolarization images; and 3) multifrequency and multipolarization images A recently developed dynamic learning neural network was adopted as the classifier Results show that using partial information, P-band multipolarization images and multiband hh polarization images have better classification accuracy, while with a full configuration, namely, multiband and multipolarization, gives the best discrimination capability The overall accuracy using the proposed method can be as high as 95% with a total of thirteen cover types classified Further reduction of the data volume by means of correlation analysis was conducted to single out the minimum data channels required It was found that this method efficiently reduces the data volume while retaining highly acceptable classification accuracy
TL;DR: In this article, a semiautomatic method for detecting the shoreline accurately and efficiently in ERS-1 SAR images is presented, aimed primarily at a particular application, namely the construction of a digital elevation model of an intertidal zone using SAR images and hydrodynamic model output, but could be carried over to other applications.
Abstract: Extraction of the shoreline in SAR images is a difficult task to perform using simple image processing operations such as grey-value thresholding, due to the presence of speckle and because the signal returned from the sea surface may be similar to that from the land. A semiautomatic method for detecting the shoreline accurately and efficiently in ERS-1 SAR images is presented. This is aimed primarily at a particular application, namely the construction of a digital elevation model of an intertidal zone using SAR images and hydrodynamic model output, but could be carried over to other applications. A coarse-fine resolution processing approach is employed, in which sea regions are first detected as regions of low edge density in a low resolution image, then image areas near the shoreline are subjected to more elaborate processing at high resolution using an active contour model. Over 90% of the shoreline detected by the automatic delineation process appear visually correct.
TL;DR: The SAR-based classification of an ERS-1/JERS-1 composite is superior to unsupervised classification of multitemporal AVHRR data supplemented with a priori information on elevation, climate, and ecoregion.
Abstract: Land-cover classification of an ERS-1/JERS-1 composite is explored in the context of regional- to global-scale applicability. Each of these orbiting synthetic aperture radars provide somewhat complementary information since data is collected using significantly different frequencies, polarizations, and look angles (ERS-1: C-band, VV polarization, 23/spl deg/; JERS-1: L-band, HH polarization, 35/spl deg/). This results in a classification procedure for the composite image (a co-registered pair from the same season) that is superior to that obtained from either of the two sensors alone. A conceptual model is presented to show how simple structural attributes of terrain surfaces and vegetation cover relate to the data from these two sensors. The conceptual model is knowledge based; and it is supported by both theoretical considerations and experimental observations. The knowledge-based, conceptual model is incorporated into a classifier that uses hierarchical decision rules to differentiate land-cover classes. The land-cover classes are defined on the basis of generalized structural properties of widespread applicability. The classifier operates sequentially and produces two levels of classification. At level-2, terrain is structurally differentiated into man-made features (urban), surfaces, short vegetation, and tall vegetation. At level-2, the tall vegetation class is differentiated on the basis of plant architectural properties of the woody stems and foliage. Growth forms of woody stems include excurrent (i.e., pines), decurrent (i.e., oaks), and columnar (i.e., palm) architecture. Two classes of leaves are considered: broadleaf and needle-leaf. The composite classifier yields overall accuracies in excess of 90% for a test site in northern Michigan located along the southern ecotone of the boreal forest. For the area examined, the SAR-based classification is superior to unsupervised classification of multitemporal AVHRR data supplemented with a priori information on elevation, climate, and ecoregion.
TL;DR: An automated algorithm intended for operational use is developed and tested for estimating wind speed and direction using ERS-1 SAR imagery and utilizing these estimated wind directions from the SAR imagery subsequently improves wind speed estimation.
Abstract: An automated algorithm intended for operational use is developed and tested for estimating wind speed and direction using ERS-1 SAR imagery. The wind direction comes from the orientation of low frequency, linear signatures in the SAR imagery that the authors believe are manifestations of roll vortices within the planetary boundary layer. The wind direction thus has inherently a 180/spl deg/ ambiguity since only a single SAR image is used. Wind speed is estimated by using a new algorithm that utilizes both the estimated wind direction and /spl sigma//sub 0/ values to invert radar cross section models. The authors show that: 1) on average the direction of the roll vortices signatures is approximately 11/spl deg/ to the right of the surface wind direction and can be used to estimate the surface wind direction to within /spl plusmn/19/spl deg/ and 2) utilizing these estimated wind directions from the SAR imagery subsequently improves wind speed estimation, generating errors of approximately /spl plusmn/1.2 m/s, for ERS-1 SAR data collected during the Norwegian Continental Shelf Experiment in 1991.
TL;DR: The statistics of the Stokes parameters and of the phase difference are derived as a function of the mean effective phase difference and the degree of coherence for one-look and multilook SAR data.
Abstract: Barakat [1987] derived the Stokes parameter statistics, applicable to one-look synthetic aperture radar (SAR) images, of a partially polarized wave backscattered from a Gaussian area. In this paper, the statistics of the Stokes parameters and of the phase difference are derived as a function of the mean effective phase difference and the degree of coherence for one-look and multilook SAR data. The statistics of the degree of coherence are also derived for multilook SAR data. It is shown that the estimator currently used for calculation of the degree of coherence is biased under low coherence conditions.
TL;DR: This note shows that the method is completely equivalent to extracting proportional ground cover by standard means, but is less efficient than the more usual methods of spectral unmixing.
Abstract: A linear transformation was recently recommended for application on hyperspectral imagery. This note shows that the method is completely equivalent to extracting proportional ground cover by standard means, but is less efficient than the more usual methods of spectral unmixing.
TL;DR: The formalism of the one-dimensional discrete wavelet transform (DWT) based on Daubechies wavelet filters is outlined in terms of finite vectors and matrices and the simulated effects of poor instrument resolution on the estimated lead number density and the mean lead width are investigated.
Abstract: The formalism of the one-dimensional discrete wavelet transform (DWT) based on Daubechies wavelet filters is outlined in terms of finite vectors and matrices. Both the scale-dependent wavelet variance and wavelet covariance are considered and confidence intervals for each are determined. The variance estimates are more accurately determined with a maximal-overlap version of the wavelet transform. The properties of several Daubechies wavelet filters and the associated basis vectors are discussed. Both the Mallat orthogonal-pyramid algorithm for determining the DWT and a pyramid algorithm for determining the maximal-overlap version of the transform are presented in terms of finite vectors. As an example, the authors investigate the scales of variability of the surface temperature and albedo of spring pack ice in the Beaufort Sea. The data analyzed are from individual lines of a Landsat TM image (25-m sample interval) and include both reflective (channel 3, 30-m resolution) and thermal (channel 6, 120-m resolution) data. The wavelet variance and covariance estimates are presented and more than half of the variance is accounted for by scales of less than 800 m. A wavelet-based technique for enhancing the lower-resolution thermal data using the reflected data is introduced. The simulated effects of poor instrument resolution on the estimated lead number density and the mean lead width are investigated using a wavelet-based smooth of the observations.
TL;DR: A processing technique for polarimetric synthetic aperture radar (SAR) data has been developed which produces profiles of terrain slopes and elevations in the azimuthal (or along-track) direction which estimates the average shift in orientation angle of copolarization backscatter caused by azimUTHal tilts of the scattering plane.
Abstract: A processing technique for polarimetric synthetic aperture radar (SAR) data has been developed which produces profiles of terrain slopes and elevations in the azimuthal (or along-track) direction. This technique estimates the average shift in orientation angle of copolarization backscatter caused by azimuthal tilts of the scattering plane. Using P-band data, tests of this technique have been made for an area in the Black Forest near Villingen/Schwenningen in Baden-Wurttemberg, Germany. The radar measured slope and derived elevation profiles have low rms errors and high correlation values when compared with a stereo-photograph digital-elevation map (DEM) for the area. This algorithm is capable of adaptively making transitions from the forested areas to nearby regions with open-terrain. Subsequent tests of the algorithm have been conducted using polarimetric SAR L-band data for a mountainous, nonforested, region in the Mojave Desert (Ft. Irwin, CA) where an accurate DEM also was available. Complete elevation and slope mapping of the terrain in two dimensions using this technique is possible when azimuthal elevation profiles are produced throughout the range extent of the SAR image.
TL;DR: The authors discuss an efficient phase preserving technique for ScanSAR focusing that can be significantly reduced by means of an azimuth varying filter, and the SAR-ScanSAR interferometry is proposed: here the decorrelation can always be removed.
Abstract: The authors discuss an efficient phase preserving technique for ScanSAR focusing, used to obtain images suitable for ScanSAR interferometry. Given two complex focused ScanSAR images of the same area, an interferogram can be generated as for conventional repeat pass SAR interferometry. However, due to the nonstationary azimuth spectrum of ScanSAR images, the coherence of the interferometric pair and the interferogram resolution are affected, both by the possible scan misregistration between two passes and by the terrain slopes along the azimuth. The resulting decorrelation can be significantly reduced by means of an azimuth varying filter, provided that some conditions on the scan misregistration are met. Finally, the SAR-ScanSAR interferometry is proposed: here the decorrelation can always be removed. With no resolution loss by means of the technique presented.
TL;DR: In this paper, the authors exploit the interferometric multifrequency potentiality of the SIR-C/X-SAR system which is equipped with an L-, C-, and X-band sensor.
Abstract: The authors exploit the interferometric multifrequency potentiality of the SIR-C/X-SAR system which is equipped with an L-, C-, and X-band sensor. They present a solution to improve the unwrapping performance of the C- and X-band data by considering the L-band unwrapped pattern. A new algorithm for the generation of a single digital elevation model (DEM) combining L-, C-, and X-band information is presented. This solution is based on the fusion of the unwrapped phase patterns by using a Kalman filter. The proposed fusion operation also accounts for the coherence characteristics of the three data sets. The selected test site is the Mt. Etna region in Italy which is very interesting from the volcanological and geological point of view. Numerical assessments of the achieved results are provided by evaluating the height accuracy with respect to a reference DEM.
TL;DR: This paper addresses a new procedure for phase unwrapping especially designed for interferometric synthetic aperture radar applications based on use of Green's first identity.
Abstract: Any method that permits retrieving full range (unwrapped) phase values starting from their (-/spl pi/,/spl pi/) determination (wrapped phase) can be defined as a phase unwrapping technique. This paper addresses a new procedure for phase unwrapping especially designed for interferometric synthetic aperture radar applications. The proposed algorithm is based on use of Green's first identity. Results on simulated as well as on real data are presented. They both confirm the excellent performance of the procedure.
TL;DR: It is shown that the fact that the burst cycle period is in general not an integer multiple of the sampling grid distance does not complicate the algorithm, and an image example using X-SAR data for simulation of a burst system is presented.
Abstract: Processing ScanSAR or burst-mode SAR data by standard high precision algorithms (e.g., range/Doppler, wavenumber domain, or chirp scaling) is shown to be an interesting alternative to the normally used SPECAN (or deramp) algorithm. Long burst trains with zeroes inserted into the interburst intervals can be processed coherently. This kind of processing preserves the phase information of the data-an important aspect for ScanSAR interferometry. Due to the interference of the burst images the impulse response shows a periodic modulation that can be eliminated by a subsequent low-pass filtering of the detected image. This strategy allows an easy and safe adaptation of existing SAR processors to ScanSAR data if throughput is not an issue. The images are automatically consistent with regular SAR mode images both with respect to geometry and radiometry. The amount and diversity of the software for a multimode SAR processor are reduced. The impulse response and transfer functions of a burst-mode end-to-end system are derived. Special attention is drawn to the achievable image quality, the radiometric accuracy, and the effective number of looks. The scalloping effect known from burst-mode systems can be controlled by the spectral weighting of the processor transfer function. It is shown that the fact that the burst cycle period is in general not an integer multiple of the sampling grid distance does not complicate the algorithm. An image example using X-SAR data for simulation of a burst system is presented.