Showing papers on "Radar published in 2009"
TL;DR: A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N times N grid and the techniques of compressed sensing are employed to reconstruct the target scene.
Abstract: A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N times N grid. Assuming the number of targets K is small (i.e., K Lt N2), then we can transmit a sufficiently ldquoincoherentrdquo pulse and employ the techniques of compressed sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel-compressed sensing approach offers great potential for better resolution over classical radar.
1,113 citations
TL;DR: An ultrawideband (UWB) microwave system for breast cancer detection is presented, demonstrating the successful detection of 4 and 6 mm diameter spherical tumors in the curved breast phantom.
Abstract: In this contribution, an ultrawideband (UWB) microwave system for breast cancer detection is presented. The system is based on a novel hemispherical real-aperture antenna array, which is employed in a multi-static radar-based detection system. The array consists of 16 UWB aperture-coupled stacked-patch antennas located on a section of a hemisphere. The radar system is designed to be used with realistic three-dimensional (3D) breast phantoms, which have been developed, as well as with real breast cancer patients during initial clinical trials. Images are formed using two different beamforming algorithms and the performance of these algorithms is firstly compared through numerical simulation. Experimental results for the same beamforming techniques are then presented, demonstrating the successful detection of 4 and 6 mm diameter spherical tumors in the curved breast phantom.
430 citations
TL;DR: A novel multistage approach is developed for disturbance cancellation and target detection based on projections of the received signal in a subspace orthogonal to both the disturbance and previously detected targets.
Abstract: The paper examines the problem of cancellation of direct signal, multipath and clutter echoes in passive bistatic radar (PBR). This problem is exacerbated as the transmitted waveform is not under control of the radar designer and the sidelobes of the ambiguity function can mask targets including those displaced in either (or both) range and Doppler from the disturbance. A novel multistage approach is developed for disturbance cancellation and target detection based on projections of the received signal in a subspace orthogonal to both the disturbance and previously detected targets. The resulting algorithm is shown to be effective against typical simulated scenarios with a limited number of stages, and a version with computational savings is also introduced. Finally its effectiveness is demonstrated with the application to real data acquired with an experimental VHF PBR system.
412 citations
Book•
31 Jan 2009
TL;DR: Part I Introduction to LPI radar and waveform design: to see and not be seen LPI technology and applications ambiguity analysis of LPI waveforms FMCW radar phase shift keying techniques frequency shift keies techniques case study - anti-ship LPI missile seeker.
Abstract: Part I Introduction to LPI radar and waveform design: to see and not be seen LPI technology and applications ambiguity analysis of LPI waveforms FMCW radar phase shift keying techniques frequency shift keying techniques case study - anti-ship LPI missile seeker. Part II Intercept receiver strategies and signal processing: strategies for LPI radar interception Wigner distribution analysis of LPI waveforms LPI radar analysis using quadrature mirror filtering cyclostationary bi-frequency analysis of LPI radar waveforms. Appendix: MATLAB software.
409 citations
TL;DR: This version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic.
Abstract: This paper contains a description of the most recent version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). This version contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic. These modifications include the estimation of confidence factors that characterize the possible impacts of all error sources on radar measurements, the assignment of the matrix of weights that characterizes the classification power of each variable with respect to every class of radar echo, and the implementation of a class designation system based on the distance from the radar and the parameters of the melting layer that are determined as functions of azimuth with polarimetric radar measurements. These additions provide considerable flexibility and improve the discrimination between liquid and frozen hydrometeors. The new classification scheme utilizes all available polarimetric va...
408 citations
TL;DR: In this article, the effect of other factors such as cross-section configuration in one-dimensional (1D) models, mesh resolution in two-dimensional models (2D), representation of river bathymetry, and modeling approach is studied or documented.
404 citations
20 Nov 2009
TL;DR: The Digital Signal Processing Handbook (DSP) as mentioned in this paper provides a comprehensive overview of signal processing related to wireless, radar, spacetime coding, and mobile communications, together with associated applications to networking, storage, and communications.
Abstract: Now available in a three-volume set, this updated and expanded edition of the bestselling The Digital Signal Processing Handbook continues to provide the engineering community with authoritative coverage of the fundamental and specialized aspects of information-bearing signals in digital form. Encompassing essential background material, technical details, standards, and software, the second edition reflects cutting-edge information on signal processing algorithms and protocols related to speech, audio, multimedia, and video processing technology associated with standards ranging from WiMax to MP3 audio, low-power/high-performance DSPs, color image processing, and chips on video. Drawing on the experience of leading engineers, researchers, and scholars, the three-volume set contains 29 new chapters that address multimedia and internet technologies, tomography, radar systems, architecture, standards, and future applications in speech, acoustics, video, radar, and telecommunications. This volume, Wireless, Networking, Radar, Sensor Array Processing, and Nonlinear Signal Processing, provides complete coverage of the foundations of signal processing related to wireless, radar, spacetime coding, and mobile communications, together with associated applications to networking, storage, and communications.
363 citations
TL;DR: New computationally efficient cyclic algorithms for MIMO radar waveform synthesis can be used for the design of unimodular MIMo sequences that have very low auto- and cross-correlation sidelobes in a specified lag interval, and of very long sequences that could hardly be handled by other algorithms previously suggested in the literature.
Abstract: A multiple-input multiple-output (MIMO) radar system that transmits orthogonal waveforms via its antennas can achieve a greatly increased virtual aperture compared with its phased-array counterpart. This increased virtual aperture enables many of the MIMO radar advantages, including enhanced parameter identifiability and improved resolution. Practical radar requirements such as unit peak-to-average power ratio and range compression dictate that we use MIMO radar waveforms that have constant modulus and good auto- and cross-correlation properties. We present in this paper new computationally efficient cyclic algorithms for MIMO radar waveform synthesis. These algorithms can be used for the design of unimodular MIMO sequences that have very low auto- and cross-correlation sidelobes in a specified lag interval, and of very long sequences that could hardly be handled by other algorithms previously suggested in the literature. A number of examples are provided to demonstrate the performances of the new waveform synthesis algorithms.
360 citations
TL;DR: In this article, a method for retrieving precipitation over the ocean using spaceborne W-band radar is introduced and applied to the CloudSat Cloud Profiling Radar, which is most applicable to stratiform-type precipitation.
Abstract: [1] A method for retrieving precipitation over the ocean using spaceborne W-band (94 GHz) radar is introduced and applied to the CloudSat Cloud Profiling Radar. The method is most applicable to stratiform-type precipitation. Measurements of radar backscatter from the ocean surface are combined with information about surface wind speed and sea surface temperature to derive the path-integrated attenuation through precipitating cloud systems. The scattering and extinction characteristics of raindrops are modeled using a combination of Mie theory (for raindrops) and the discrete dipole approximation (for ice crystals and melting snow), and a model of the melting layer is implemented to represent the transition between ice and liquid water. Backward Monte Carlo modeling is used to model multiple scattering from precipitating hydrometeors between the radar and ocean surface, which is shown to be significant for precipitation rates exceeding 3–5 mm h−1, particularly when precipitating ice is present. An uncertainty analysis is presented and the algorithm is applied to near-global CloudSat observations and compared with other near-global precipitation sources. In the tropics, CloudSat tends to underestimate the heaviest precipitation. It is found that in the middle latitudes, however, CloudSat observes precipitation more often and with greater resulting accumulation than other spaceborne sensors.
327 citations
26 Apr 2009
TL;DR: A novel approach to OFDM radar processing is introduced that overcomes the typical drawbacks of correlation based processing and a suitable OFDM system parameterization for operation at 24 GHz is derived that fulfills the requirements for both applications.
Abstract: In this paper the possibility of designing an OFDM system for simultaneous radar and communications operations is discussed. A novel approach to OFDM radar processing is introduced that overcomes the typical drawbacks of correlation based processing. A suitable OFDM system parameterization for operation at 24 GHz is derived that fulfills the requirements for both applications. The operability of the proposed system concept is verified with MatLab simulations. Keywords-OFDM; Radar, Communications
301 citations
TL;DR: In this paper, a new remote sensing retrieval of ice cloud microphysics has been developed for use with millimeter-wave radar from ground-, air-, or space-based sensors.
Abstract: [1] A new remote sensing retrieval of ice cloud microphysics has been developed for use with millimeter-wave radar from ground-, air-, or space-based sensors. Developed from an earlier retrieval that used measurements of radar reflectivity factor together with a priori information about the likely cloud targets, the new retrieval includes temperature information as well to assist in determining the correct region of state space, particularly for those size distribution parameters that are less constrained by the radar measurements. These algorithms have served as the ice cloud retrieval algorithms in Releases 3 and 4 of the CloudSat 2B-CWC-RO Standard Data Product. Several comparison studies have been performed on the previous and current retrieval algorithms: some involving tests of the algorithms on simulated radar data (based on actual cloud probe data or cloud resolving models) and others featuring statistical comparisons of the R04 2B-CWC-RO product (current algorithm) to ice cloud mass retrievals by other spaceborne, airborne, and ground-based instruments or alternative algorithms using the same CloudSat radar data. Comparisons involving simulated radar data based on a database of cloud probe data showed generally good performance, with ice water content (IWC) bias errors estimated to be less than 40%. Comparisons to ice water content and ice water path estimates by other instruments are mixed. When the comparison is restricted to different retrieval approaches using the same CloudSat radar measurements, CloudSat R04 results generally agree with alternative IWC retrievals for IWC < 1000 mg m−3 at altitudes below 12 km but differ at higher ice contents and altitudes, either exceeding other retrievals or falling within a spread of retrieval values. Validation and reconciliation of all these approaches will continue to be a topic for further research.
TL;DR: In this article, the basic structure and flow of the rain profiling algorithm for the TRMM Precipitation Radar is described, and the major assumptions and sources of error in the algorithm are discussed.
Abstract: This paper describes the basic structure and flow of the rain profiling algorithm for the TRMM Precipitation Radar, and discusses the major assumptions and sources of error in the algorithm. In particular, it describes how the uncertainties in individual parameters affect the attenuation correction and rain estimates. Major parameters involved are the drop size distribution, the phase state of precipitating particles, their density and shape, inhomogeneity of precipitation distribution within the footprint, attenuation due to cloud liquid water and water vapor, freezing height, uncertainty of the surface scattering cross section, and fluctuation of the radar echo signal. Among these parameters that affect the rain estimates, the effect of inhomogeneity of rain distribution is summarized in detail. The paper also describes how these parameters are taken into account in different versions of the standard algorithm 2A25.
TL;DR: A novel iterative algorithm is proposed to optimize the waveforms and receiving filters in the MIMO radar such that the detection performance can be maximized and these algorithms have better SINR performance than existing design methods.
Abstract: The concept of multiple-input multiple-output (MIMO) radar allows each transmitting antenna element to transmit an arbitrary waveform. This provides extra degrees of freedom compared to the traditional transmit beamforming approach. It has been shown in the recent literature that MIMO radar systems have many advantages. In this paper, we consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. Numerical results show that the proposed methods have better SINR performance than existing design methods.
TL;DR: In this article, the authors evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatistical merging techniques, and the results show that the geostatic merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data.
Abstract: . Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatistical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to estimate daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 4-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical merging methods give the best results for all tested network densities and their relative benefit increases with the network density.
04 May 2009
TL;DR: The NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is advancing a new approach to radar network design based on dense networks of short-range radars, which has the potential to supplement - or perhaps replace - the large long-range civil infrastructure radars in use today.
Abstract: The NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is advancing a new approach to radar network design based on dense networks of short-range radars. The center's concept is to deploy small radars atop communication towers, rooftops, and other elements of the infrastructure as a means to comprehensively map winds, rainfall, and other atmospheric and airborne objects throughout the atmosphere with resolution, low-altitude coverage, Doppler wind vector measurement, and other capabilities that are substantially beyond the current state-of-the-art. The technology has the potential to supplement - or perhaps replace - the large long-range civil infrastructure radars in use today.
TL;DR: In this article, a review of the state of the art for incorporating information on vegetation 3D structure into biodiversity and habitat science and management approaches, with emphasis on use of lidar and radar data.
Abstract: [1] Biodiversity and habitat face increasing pressures due to human and natural influences that alter vegetation structure. Because of the inherent difficulty of measuring forested vegetation three-dimensional (3-D) structure on the ground, this important component of biodiversity and habitat has been, until recently, largely restricted to local measurements, or at larger scales to generalizations. New lidar and radar remote sensing instruments such as those proposed for spaceborne missions will provide the capability to fill this gap. This paper reviews the state of the art for incorporatinginformation on vegetation 3-D structure into biodiversity and habitat science and management approaches, with emphasis on use of lidar and radar data. First we review relationships between vegetation 3-D structure, biodiversity and habitat, and metrics commonly used to describe those relationships. Next, we review the technical capabilities of new lidar and radar sensors and their application to biodiversity and habitat studies to date. We then define variables that have been identified as both useful and feasible to retrieve from spaceborne lidar and radar observations and provide their accuracy and precision requirements. We conclude with a brief discussion of implications for spaceborne missions and research programs. The possibility to derive vegetation 3-D measurements from spaceborne active sensors and to integrate them into science and management comes at a critical juncture for global biodiversity conservation and opens new possibilities for advanced scientific analysis of habitat and biodiversity.
Book•
08 Oct 2009
TL;DR: Inverse synthetic-aperture radar as mentioned in this paper is a type of radar that can be used for both two-dimensional and three-dimensional wave propagation in two and three dimensions.
Abstract: List of figures List of tables Preface Part I. Radar Basic: 1. Introduction 2. Radar systems 3. Introduction to scattering 4. Detection of signals in noise 5. The radar ambiguity function Part II. Radar Imaging: 6. Wave propagation in two and three dimensions 7. Inverse synthetic-aperture radar 8. Antennas 9. Synthetic-aperture radar 10. Related techniques 11. Open problems Bibliography Index.
TL;DR: The performance of the 1.5-μm pulsed Doppler lidar, operated by the U.K. Universities Facility for Atmospheric Measurement (UFAM) over a 51-day continuous and unattended field deployment in southern England, is described and analyzed with a view to demonstrating the capabilities of the system for remote measurements of aerosols and velocities in the boundary layer as discussed by the authors.
Abstract: The performance of the 1.5-μm pulsed Doppler lidar, operated by the U.K. Universities Facility for Atmospheric Measurement (UFAM) over a 51-day continuous and unattended field deployment in southern England, is described and analyzed with a view to demonstrating the capabilities of the system for remote measurements of aerosols and velocities in the boundary layer. A statistical assessment of the vertical pointing mode in terms of the availability and errors in the data versus range is presented. Examples of lidar data are compared to theoretical predictions, radiosondes, the UFAM radar wind profiler, and an ultrasonic anemometer.
TL;DR: Critical performance parameters such as the ldquovisibility of the groundrdquo at L- and P-band as well as temporal decorrelation in short-time repeat-pass interferometry are discussed and quantitatively assessed.
Abstract: This paper addresses the potential and limitations of polarimetric synthetic aperture radar (SAR) interferometry (Pol-InSAR) inversion techniques for quantitative forest-parameter estimation in tropical forests by making use of the unique data set acquired in the frame of the second Indonesian Airborne Radar Experiment (INDREX-II) campaign - including Pol-InSAR, light detection and ranging (LIDAR), and ground measurements - over typical Southeast Asia forest formations. The performance of Pol-InSAR inversion is not only assessed primarily at L- and P-band but also at higher frequencies, namely, X-band. critical performance parameters such as the ldquovisibility of the groundrdquo at L- and P-band as well as temporal decorrelation in short-time repeat-pass interferometry are discussed and quantitatively assessed. Inversion performance is validated against LIDAR and ground measurements over different test sites.
Journal Article•
TL;DR: This paper develops a model function that expresses copolarized backscattering cross sections (sigmahh and sigmavv) in terms of volumetric soil moisture using L-band experimental data for both bare and vegetated surfaces and proposes a viable approach to determine these two unknowns using combined radiometer and radar data.
Abstract: Electromagnetic scattering from a rough surface is a function of both surface roughness and dielectric constant of the scattering surface. Therefore, in order to estimate soil moisture of a bare surface accurately from radar measurements, the effects of surface roughness must be compensated for properly. Several algorithms have been developed to estimate soil moisture from a polarimetric radar image, all with limited ranges of applicability. No theoretical algorithm has been reported to retrieve volumetric soil moisture of a vegetated surface. In this paper, we examine a different approach to estimate soil moisture that exploits the fact that the backscattering cross section from a natural object changes over short timescales mainly due to variations in soil moisture. We develop a model function that expresses copolarized backscattering cross sections (sigmahh and sigmavv) in terms of volumetric soil moisture using L-band experimental data for both bare and vegetated surfaces. In order to estimate soil moisture, two unknowns in the model function must be determined. We propose a viable approach to determine these two unknowns using combined radiometer and radar data. This time-series approach also provides a framework to utilize a priori knowledge on soil moisture to improve the retrieval accuracy of volumetric soil moisture. We demonstrate that this time-series algorithm is a simple and robust way to estimate soil moisture for both bare and vegetated surfaces.
TL;DR: This paper describes a novel frequency-modulated continuous-wave radar concept, where methods like nonuniform sparse antenna arrays and multiple-input multiple-output techniques are used to improve the angular resolution of the proposed system.
Abstract: This paper describes a novel frequency-modulated continuous-wave radar concept, where methods like nonuniform sparse antenna arrays and multiple-input multiple-output techniques are used to improve the angular resolution of the proposed system. To demonstrate the practical feasibility using standard production techniques, a prototype sensor using a novel four-channel single-chip radar transceiver in combination with differential patch antenna arrays was realized on off-the-shelf RF substrate. Furthermore, to demonstrate its practical applicability, the assembled system was tested in real world measurement scenarios in conjunction with the presented efficient signal processing algorithms.
TL;DR: A novel 3-D MOCO method is proposed to extract necessary motion parameters from radar raw data, based on an instantaneous Doppler rate estimate, suitable for low- or medium-altitude UAV SAR systems equipped with a low-accuracy inertial navigation system.
Abstract: Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is very important for battlefield awareness. For SAR systems mounted on a UAV, the motion errors can be considerably high due to atmospheric turbulence and aircraft properties, such as its small size, which makes motion compensation (MOCO) in UAV SAR more urgent than other SAR systems. In this paper, based on 3-D motion error analysis, a novel 3-D MOCO method is proposed. The main idea is to extract necessary motion parameters, i.e., forward velocity and displacement in line-of-sight direction, from radar raw data, based on an instantaneous Doppler rate estimate. Experimental results show that the proposed method is suitable for low- or medium-altitude UAV SAR systems equipped with a low-accuracy inertial navigation system.
TL;DR: Experimental results show that the proposed pattern-recognition system to identify and classify buried objects from ground-penetrating radar (GPR) imagery exhibits promising performances both in terms of object detection and material recognition.
Abstract: In this paper, we propose a novel pattern-recognition system to identify and classify buried objects from ground-penetrating radar (GPR) imagery. The entire process is subdivided into four steps. After a preprocessing step, the GPR image is thresholded to put under light the regions containing potential objects. The third step of the system consists of automatically detecting the objects in the obtained binary image by means of a search of linear/hyperbolic patterns formulated within a genetic optimization framework. In the genetic optimizer, each chromosome models the apex position and the curvature associated with the candidate pattern, while the fitness function expresses the Hamming distance between that pattern and the binary image content. Finally, in the fourth step, the problem of the recognition of the material type of the identified objects is approached as a classification issue, which is solved by means of an opportune feature-extraction strategy and a support vector machine classifier. To illustrate the performances of the proposed system, we conducted a thorough experimental study based on GPR images generated by a GPR simulator based on the finite-difference time-domain method so as to construct different acquisition scenarios by varying the number of buried objects, their position, their size, their shape, and their material type. In general, the obtained experimental results show that the proposed system exhibits promising performances both in terms of object detection and material recognition.
TL;DR: In this paper, Doppler radar uses double-sideband transmission for vital sign detection from four sides of a human body, where an unmodulated radio-frequency signal is transmitted toward the human body where it is phasemodulated by the periodic physiological movement and reflected back to the receiver.
Abstract: Medical technology has improved remarkably over the past few generations, becoming more sophisticated and less invasive as the years progress. Now, with microwave Doppler radar phase modulation, noncontact respiration and heartbeat monitoring offers an attractive alternative to commonly prescribed chest-strap monitors. Doppler radar uses double-sideband transmission for vital sign detection from four sides of a human body. An unmodulated radio-frequency signal is transmitted toward the human body, where it is phase-modulated by the periodic physiological movement and reflected back to the receiver. The radar receiver captures the reflected signal and demodulates it to extract the vital sign signal components.
TL;DR: In this paper, a real-time implementation of the radar ensemble generator coupled with a semi-distributed hydrological model in the framework of the forecast demonstration project MAP D-PHASE is presented.
Abstract: An elegant solution to characterise the residual errors in radar precipitation estimates is to generate an ensemble of precipitation fields. The paper proposes a radar ensemble generator designed for usage in the Alps using LU decomposition (REAL), and presents first results from a real-time implementation coupling the radar ensemble with a semi-distributed rainfall-runoff model for flash flood modelling in a steep Alpine catchment. Each member of the radar ensemble is a possible realisation of the unknown true precipitation field given the observed radar field and knowledge of the space-time error structure of radar precipitation estimates. Feeding the alternative realisations into a hydrological model yields a distribution of response values, the spread of which represents the sensitivity of runoff to uncertainties in the input radar precipitation field. The presented ensemble generator is based on singular value decomposition of the error covariance matrix, stochastic simulation using the LU decomposition algorithm, and autoregressive filtering. It allows full representation of spatial dependence of the mean and covariances of radar errors. This is of particular importance in a mountainous region with large uncertainty in radar precipitation estimates and strong dependence of error structure on location. The real-time implementation of the radar ensemble generator coupled with a semi-distributed hydrological model in the framework of the forecast demonstration project MAP D-PHASE is one of the first experiments of this type worldwide, and is a fully novel contribution to this evolving area of applied research. Copyright c ! 2009 Royal Meteorological Society
TL;DR: A joint direction of arrivals (DOAs) and direction of departures (DODs) estimation algorithm for bistatic multiple-input multiple-output (MIMO) radar via ESPRIT by means of the rotational factor produced by multi-transmitter is presented.
TL;DR: Results seem to indicate that the methodology of kriging with external drift [KED], in combination with the technique of automatically computing 2-D spatial correlograms, provides merged rainfall fields with good agreement with rain gauges and with the most accurate approach to the spatial tendencies observed in the radar rainfall fields, when compared with other alternatives analyzed.
TL;DR: The results reported in this paper emphasize the value of polarimetric, as well as multifrequency SAR, data for crop classification, with such a diverse capability, a SAR-only approach to crop classification becomes increasingly viable.
Abstract: Mapping and monitoring changes in the distribution of cropland provide information that aids sustainable approaches to agriculture and supports early warning of threats to global and regional food security. This paper tested the capability of Phased Array type L-band Synthetic Aperture Radar (SAR) (PALSAR) multipolarization and polarimetric data for crop classification. L-band results were compared with those achieved with a C-band SAR data set (ASAR and RADARSAT-1), an integrated C- and L-band data set, and a multitemporal optical data set. Using all L-band linear polarizations, corn, soybeans, cereals, and hay-pasture were classified to an overall accuracy of 70%. A more temporally rich C-band data set provided an accuracy of 80%. Larger biomass crops were well classified using the PALSAR data. C-band data were needed to accurately classify low biomass crops. With a multifrequency data set, an overall accuracy of 88.7% was reached, and many individual crops were classified to accuracies better than 90%. These results were competitive with the overall accuracy achieved using three Landsat images (88.0%). L-band parameters derived from three decomposition approaches (Cloude-Pottier, Freeman-Durden, and Krogager) produced superior crop classification accuracies relative to those achieved using the linear polarizations. Using the Krogager decomposition parameters from all three PALSAR acquisitions, an overall accuracy of 77.2% was achieved. The results reported in this paper emphasize the value of polarimetric, as well as multifrequency SAR, data for crop classification. With such a diverse capability, a SAR-only approach to crop classification becomes increasingly viable.
Book•
28 Oct 2009
TL;DR: In this paper, the technology of radar imaging for remote sensing applications is treated in a manner suited to the mathematical background of most earth scientists, where the authors assume no prior knowledge of radar on the part of the reader; instead it is assumed that the radars of interest are, in general, multi-polarized.
Abstract: This book treats the technology of radar imaging for remote sensing applications in a manner suited to the mathematical background of most earth scientists. It assumes no prior knowledge of radar on the part of the reader; instead it commences with a development of the essential concepts of radar before progressing through to a detailed coverage of contemporary ideas such as polarimetry and interferometry. Because the technology of radar imaging is potentially complex the first chapter provides a framework against which the rest of the book is set. Together, the first four chapters present the technical foundations for remote sensing with imaging radar. Scattering concepts are then covered so that the reader develops the knowledge necessary for interpreting radar data, itself the topic of a later chapter which draws together the current thinking in the analysis of radar imagery. The treatment is based on the assumption that the radars of interest are, in general, multi-polarised. Polarisation synthesis and polarised interferometric SAR are among the topics covered, as are tomography and the various forms of interferometry. A full chapter is given to bistatic radar, which is now emerging as an imaging technology with enormous potential and flexibility in remote sensing. The book concludes with a summary of passive microwave imaging. A set of appendices is included that provide supplementary material, among which is an overview of the rather complicated process of image formation with synthetic aperture radar, and summaries of some of the mathematical procedures important for a full appreciation of radar as a remote sensing technology.