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The optimization of polarimetric contrast enhancement (OPCE) is one of important problems in radar polarimetry. The traditional OPCE is to choose optimal polarization states for enhancing a desired target versus an undesired target/clutter. The contrast enhancement enables us to discriminate or distinguish the desired target from background or from the undesired target. We propose a generalized OPCE (GOPCE). For the GOPCE problem, we need to find not only three coefficients such that the ratio of two factors associated with the desired target and clutter is maximal, but also optimal polarization states such that the received power ratio of the target and clutter is maximal. Both the factors consist of three parameters, i.e., the Cloude entropy (Cloude, S.R. and Pottier, E., IEEE Trans. Geosci. Remote Sensing, vol.35, p.68-78, 1997) and two special similarity parameters (Yang, J. et al., Electron. Lett., vol.37, p.193-4, 2001). The optimal coefficients of the GOPCE are obtained by solving an eigenvalue problem. Using an example, we demonstrate that the GOPCE can be employed for detecting roads in a forest area by using polarimetric SAR data.

Calibration and application of polarimetric synthetic aperture radar (SAR) onboard the Advanced Land Observing Satellite (ALOS) is discussed using airborne L-band SAR data. We find that even a simple calibration is useful for polarimetric classification, if the system has a small cross-talk and good channel valance.

During January, 1998 an unusual and devastating series of ice storms occurred in eastern Ontario and western Quebec. Many forested areas in both these provinces were seriously damaged and sustained severe structural changes which ranged from loss of limbs to being bent horizontally to the ground. The impact on the forests varied from moderate to severe and was usually a function of tree type, and the local severity of the ice storms. In this paper, an investigation is made into the possibility of using the polarization information for classification of damaged forest areas. Polarimetric synthetic aperture radar (SAR) data were collected over a scene in the Ottawa region with the Department of the Environment Convair-580. Reference point targets were deployed during the flight, and used to calibrate the four measured linear polarizations (HH, VV, HV, and VH) in magnitude and phase. Ground truth data were collected in the field and from a helicopter flight.

The potential of synthetic aperture radar (SAR) in monitoring soil and vegetation parameters is being evaluated in extensive investigations, worldwide. A significant experiment on this subject, the Multi-sensor Airborne Campaign (MAC 91), was carried out in the summer of 1991 on several sites in Europe, based on the NASA/JPL polarimetric synthetic aperture radar (AIRSAR). The site of Montespertoli (Italy) was imaged three times during this campaign at P-, L-, and C-band and at different incidence angles between 20 degrees and 50 degrees. Calibrated full polarimetric data collected over the agricultural area of this site have been analysed and a critical analysis of the information contained in linear and circular co-polar and cross-polar data has also been carried out. Here a guideline for the formulation of crop discrimination algorithms is suggested. It has been found that P-band data are rather effective only in discriminating broad classes of agricultural landscape, while finer detail can be obtained by integrating data at L- and C-bands. Indeed at L-band well developed 'broad leaf' crops can be separated from the others, whereas at C-band discrimination seems feasible in the case of moderate growth as well. Finally the sensitivity of backscattering coefficient to soil moiture and vegetation biomass is discussed.

The limiting factor affecting the performance of most airborne radar systems in detecting targets on or near the surface of the sea is return echoes from the sea surface or sea clutter. The objective of this paper is to provide an analysis of the statistics of Australian sea clutter for an L-band VV-polarised airborne radar system from sampled measured values. Polarimetric synthetic aperture radar (POLSAR) image of the North-West region of Australia, acquired in 1996 by the Jet Propulsion Laboratory (JPL) was used to study, the backscattered statistics of the sea surface. Using this POLSAR image, a number of different candidate distribution functions have been investigated, and their goodness-of-fit have been carried out using the Kolmogorov Smirnov goodness-of-fit test. The log-normal, Weibull and K-distributions have been applied to model the statistical properties of multi-look polarimetric SAR sea clutter. The K-distribution has been found to have good agreement with the observed sea clutter intensity distributions.

Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) has been extensively applied for forest parameter inversion over different frequencies and polarimetric conditions. So far, most research was based on full-pol SAR images with relatively small coverage. A spaceborne SAR system will have the potential for PolInSAR applications used for global forest monitoring. Spaceborne dual-pol SAR images usually have higher resolution and larger swath than full-pol mode. In this paper, forest height retrieval was attempted by PolInSAR from a L-band spaceborne dual-pol SAR pairs using HH and HV channels. The random volume over ground (RVoG) model was used to retrieve the height and the coherence optimization method was extended to the dual-pol PolInSAR, which makes use of polarimetry to enhance the quality of SAR interferograms. The three-stage process is also used in the dual-pol PolInSAR technique. Finally, the experimental test was performed for forest height estimation on the dual-pol L-band SAR data of the Saihanba forest acquired by the ALOS PALSAR sensor in 2009.

A method to describe dual frequency polarimetric images by determining the rigorous change in polarisation between both data sets is presented. The variation in the backscattered wave polarisation properties is parameterised in terms of eight real Gell-Mann coefficients. This method is applied on dual frequency polarimetric synthetic aperture radar data.

Technical parameter design is an effective approach to improve marine environment sounding capability for fully polarimetric synthetie apertuer radar (SAR). By analyzing geophysical contact between noise-equated backscattering coefficient and marine environment sounding, and that between radiometric resolution and marine environment sounding, we present the key technical parameters design method for fully polarimetric SAR. We first calculate radar backscattering coefficient in different marine environments by using the fully polarimetric ocean surface scattering model, and accordingly determine the noise-equated backscattering coefficient of fully polarimetric SAR ocean sounding. Then the noise-equated backscattering coefficient is used as an input parameter in SAR equation, and the function relationship between radiometric resolution and signal to noise ratio is used as a constraint condition, we thus can carry out technical parameter design, such as for signal to noise ratio, radiometric resolution and system power aperture product. By simulation calculation of fully polarimetric ocean surface scattering, we find that the noise-equated backscattering coefficient of ocean sounding is designed to -35.0 dB, which can meet the needs of fully polarimetric SAR sounding in different marine environments. Studying the function relationship between radiometric resolution and signal to noise ratio, we find that the optimal signal to noise ratio of ocean sounding is 8.0 dB. Results of C-band airborne fully polarimetric SAR design show that the above method can make technical parameter of fully polarimetric SAR meet both the need for marine environment application and system design, because of taking into account the needs of marine environment sounding.

Image contrast between extracted laver cultivation area and background water was extensively compared using various parameters that can be calculated by dual- and quad-polarization (fully polarimetric) synthetic aperture radar data. Each parameter derived from Pauli decomposition, eigenvalue analysis, coherence analysis, and four-component scattering power decomposition (4-CSPD) has distinctive characteristics and react to different backscatterers differently. Contrast comparison was made using these parameters using the L-band quad-polarization data acquired by phased-array L-band synthetic aperture radar (PALSAR) on board advanced land observing satellite (ALOS) and the X-band dual-polarization data acquired by TerraSAR-X, and experimental results showed that the contrast can be improved using multi-polarization data than using single-polarization data. It has also been found that entropy performs better among dual-polarization methods, and the surface scattering component calculated from 4-CSPD exhibits higher contrast than any other parameters from quad-polarization data.

In developing a beaconless search and rescue capability to quickly locate small aircraft that have crashed in remote areas, NASA’s Search and Rescue (S&R) Program brings together advanced polarimetric synthetic aperture radar processing, field and laboratory tests, and state‐of‐the‐art automated target detection algorithms. This paper provides the status of this program, which began with experiments conducted in concert with the JPL DC‐8 AirSAR in 1989 at the Duke University Forest. The program is being conducted by NASA’s Goddard Space Flight Center (GSFC) under the auspices of the Search and Rescue Office.

The paper deals with proposition and evaluation of new and specific methods to represent vector radar data acquired by means a side-looking measurement in order to use compression process of Lind, Buzo, Gray (LBG), and Kohonen's self organizing feature maps of topology. The aim is to enable after coding, transmission, and decoding a high-resolution reconstruction image using the Synthetic Aperture Radar (SAR) methods. The approach proposed for compression uses the statistical properties of the signals to be compressed in order to perform the vector quantification in an optimal way.

In this study fully polarimetric synthetic aperture radar (SAR) measurements are employed for sea oil slick observation. The co-polarised signature is read in terms of sea surface scattering mechanism with and without surface slicks and it is shown to be sensitive to the presence of sea oil slicks. A model, which relates the pedestal of the polarisation signature to the presence of surface slicks, is developed and successfully tested on L-band ALOS-PALSAR, C-band RADARSAT-2 and C-band SIR-C/X-SAR full-polarimetric SAR data. Experiment results show that polarisation signature, and its pedestal height, can be successfully exploited for both observing sea oil slicks and distinguishing them from weak-damping properties look-alikes.

This article compares four land cover classification methods using ALOS Palsar fully polarimetric data. Two methods are based on supervised classification and two on unsupervised classification. Both full polarimetric and intensity data were used. Fully polarimetric data gave better results (85.5%–80.8%) than intensity data only (84%–76.1%) but the differences in the overall accuracies between the methods were not more than 6%. When using intensity data only, classification results with HH and HV features were as good as using in additionally also VV, HH+VV and HH-VV bands.