C-band Polarimetric Synthetic Aperture Radar Research Articles

Estimating boreal forest species type with airborne polarimetric synthetic aperture radar

We have applied a non-parametric classifier (k nearest neighbour) to two calibrated orthogonal passes of airborne polarimetric synthetic aperture radar (POLSAR) image data over boreal forest for the purpose of discriminating canopy tree species of predefined stands. We found that a single classifier based on a single feature space (i.e. one set of POLSAR variables for all species) was less accurate than a hierarchical two-stage classifier that used different POLSAR variables for each species. We designed a two-stage classifier that first grouped stands into broad classes: pine, spruce and deciduous, and then classified each sample within the broad classes into individual species. We found that the most effective feature spaces had two or three dimensions. The two-stage classifier attained overall accuracies of between 60% and 75%. We provide a first use of an equivalency test applied to remote-sensing classification. We use Lloyd's test of equivalency to find equivalent classifiers and thus infer informative POLSAR variables. The POLSAR variables that were most informative varied between the two passes and between the various elements of the hierarchical classifier. For the initial three-class classifier the most informative POLSAR variables were the two circular polarization ratios, several of Touzi's Stokes vector variables, HHVV coherence, several texture measures such as the variance of several scattering coefficients and the order parameter of the K-distribution and characteristics of the polarization signature pedestal. These results demonstrate that C-band POLSAR has great potential for mapping boreal forest cover either on its own or in concert with other geospatial data.

Classification of crop and soil homogenous zones using multipolarization C-band SAR

With the acquisition of polarimetric radar data from satellites such as RADARSAT-2 and ALOS PALSAR, this research project explored the potential of using multipolarization C-band synthetic aperture radar (SAR) data to map homogenous soil and crop characteristics within agricultural fields. Homogenous zones within crop fields were defined using coincidental apparent soil electrical conductivity (EC) measured from a soil EC mapping system, green leaf area index (LAI) derived from airborne hyperspectral CASI data, and yield converted to phytomass at harvest (Ph) derived from a yield monitor. Airborne polarimetric C-band SAR images (5.6 cm) at 4 m spatial resolution were obtained over a test site in Ottawa, Canada, on 13 June, 26 June, and 19 July 2001. Linear (HH, VV, HV) and circular (RR, LL, RL) polarization images were synthesized from these data. Once the homogenous zones were determined by EC, LAI, and Ph, the multipolarization SAR data were classified based on these zones using single polarizations and combinations of linear and circular polarizations. Within-field classification accuracies of SAR data were higher when they were based on EC and LAI homogenous zones relative to Ph delineations. The lower SAR classification accuracies based on Ph zones were likely attributed to biomass zones defined relative to harvest rather than coincident with SAR data acquisition. LAI zones were better classified using early season SAR data when greater between-zone variability was present. EC homogenous zones were better classified using SAR data later in the growing season when soil variability was manifested in the vegetation response. Within-field classification performance over the season was dependent on the crop type and the incidence angle. At larger incidence angles (55°), results were more dependent on crop type when compared with smaller incidence angles (35°). In general, a single polarization was capable of discriminating many of the within-field homogenous zones, including EC and LAI zones in corn and LAI and Ph zones in wheat. The linear (HV) and circular (LL or RR) cross-polarizations were the best single polarizations to classify these homogenous zones. Within-field classification of the SAR into EC homogenous zones in the wheat field required polarization combinations, particularly combinations that included HH and VV polarizations. Circular polarizations, either as single polarizations or as part of a combination, were necessary to classify SAR data into EC or LAI homogenous zones as the season progressed. These results suggest that multipolarization SAR data are useful in classifying within-field homogenous zones of corn and wheat, but that this application will require access to data from sensors that synthesize circular polarizations.

Détection du gel et non-gel du sol en utilisant le radar polarimétrique à synthèse d’ouverture

Radar polarimetry is a recent field, which offers increased detection possibilities compared with the monopolarisation radar sensors. The goal of this study is to evaluate the polarimetric data potential to monitor soil freezing in an agricultural environment. With this intention, three agricultural fields were selected, in two experimental stations of Agriculture and Agri-Food Canada: Chapais and Harlaka. Data on soil temperature at three depths (1, 5, and 15 cm), as well as certain characteristics of the snow cover (density, snow height, and temperature), were collected during polarimetric image acquisitions using polarimetric C-band synthetic aperture radar (SAR) system on board Convair-580 aircraft operated by Environment Canada. Several approaches of polarimetric treatments were explored and compared: Pauli decomposition, Cloude and Pottier decomposition, polarimetric signature, and copolarized phase difference. The results obtained show that some soil surface conditions (frozen–unfrozen) can be discriminated by radar polarimetry. Indeed, with the Cloude and Pottier decomposition, this difference appears, when the soil freezes, by a decrease of the entropy values (H) and the α angle, and an increase of the anisotropy (A). The copolarisation signatures comparison shows well the differences between the pedestal height and the signature shape in the case of a frozen soil and an unfrozen one. Even if SAR polarimetry appears as a mean to discriminate soil freezing, it seems necessary to acquire a fall image, in a period without freezing, so we can compare it with the values of the parameters obtained when the soil is frozen.

Wetland characterization using polarimetric RADARSAT-2 capability

The use of single-polarization (HH) RADARSAT-1 synthetic aperture radar (SAR) data has been shown to be important for wetland water extent characterization. However, the limited capability of the RADARSAT-1 single-polarization C-band SAR in vegetation type discrimination makes the use of clear-sky-dependent visible near-infrared (VNIR) satellite data necessary for wetland mapping. In this paper, the potential of polarimetric RADARSAT-2 data for wetland characterization is investigated. The Touzi incoherent decomposition is applied for the roll-invariant decomposition of wetland scattering. In contrast with the Cloude-Pottier decomposition that characterizes target scattering type with a real entity, α, the Touzi decomposition uses a complex entity, the symmetric scattering type, for unambiguous characterization of wetland target scattering. It is shown that, like the Cloude α scattering type, the magnitude αs of the symmetric scattering is not effective for vegetation type discrimination. The phase ϕαs of the symmetric scattering type has to be used for better characterization of wetland vegetation species. The unique information provided by ϕαs for an improved wetland class discrimination is demonstrated using Convair-580 polarimetric C-band SAR data collected over the Mer Bleue wetland in the east of Ottawa, Canada. The use of ϕαs makes possible the discrimination of shrub bog from sedge fen and even permits the discrimination between conifer-dominated treed bog and upland deciduous forest under leafy conditions.

Remote Sensing of Ocean Waves by Polarimetric SAR

Abstract A new method to measure ocean wave slope spectra using fully polarimetric synthetic aperture radar (POLSAR) data was developed without the need for a complex hydrodynamic modulation transform function. There is no explicit use of a hydrodynamic modulation transfer function. This function is not clearly known and is based on hydrodynamic assumptions. The method is different from those developed by Schuler and colleagues or Pottier but complements their methods. The results estimated from NASA Jet Propulsion Laboratory (JPL) Airborne Synthetic Aperture Radar (AIRSAR) C-band polarimetric SAR data show that the ocean wavelength, wave direction, and significant wave height are in agreement with buoy measurements. The proposed method can be employed by future satellite missions such as RADARSAT-2.

Forest type discrimination using calibrated C-band polarimetric SAR data

To promote the unique polarimetric capability of RADARSAT-2, the Canada Centre for Remote Sensing (CCRS) has investigated the potential of fully polarimetric C-band synthetic aperture radar (SAR) for forest type discrimination. Six forest types in the Mer Bleu region of eastern Ontario were considered in this study: mixed forest, poplar, red pine, white pine, a mixed forest of red and white pines, and white spruce. Multi-date and multiple incidence angle acquisitions were acquired over the study site with the Convair-580 polarimetric SAR under leafy (summer) and no-leaves (winter) conditions. In addition to the conventional linear polarizations, HH, HV, and VV, various polarimetric discriminators, which were synthesized from the scattering matrix measurements, were investigated. It is shown that the radiometric intensity of the conventional C-band SAR polarizations HH, HV, and VV can only perform a limited discrimination of various tree species. The polarization information provided by fully polarimetric SARs clearly improves forest type discrimination under leafy and no-leaves conditions and permits the demonstration of the significance of SAR illumination angle on forest scattering mechanisms.

Detection of crashed aircraft in polarimetric imagery

This manuscript presents studies examining the use of C-band polarimetric Synthetic Aperture Radar (SAR) systems for the detection of crashed aircraft. The ultimate aim is to assist Search and Rescue in Canada in the location of such targets. Detection methodologies based on the Polarimetric Whitening Filter, Cameron Decomposition, and measures of even bounce contributions to the backscatter have been examined. Tests were performed using imagery of serviceable and crashed aircraft and crashed aircraft parts. Although individual methods make it possible to detect the crashed aircraft, best results for target detection with decreased numbers of false alarms occur when these methods are used in combination.

Land use mapping with evidential fusion of features extracted from polarimetric synthetic aperture radar and hyperspectral imagery

Abstract As part of the Earth Observation Application Development Program (EOADP) program sponsored by the Canada Space Agency, Lockheed Martin Canada––in partnership with Defense Research and Development Canada––has initiated a study aiming at studying the exploitation of the synergy between polarimetric SAR and hyperspectral imagery with the development of a system prototype called Intelligent Data Fusion System (IDFS). This system, based on the evidential fusion of features extracted from each type of imagery, is made of three modules. The polarimetric SAR module contains polarimetric classifiers, textural classifiers to provide hypotheses related to the textural and scattering properties of the illuminated area. The hyperspectral module contains an end-members selection technique and a collection of spectral indices. Each feature giving an incomplete representation of an object of interest, it is expected that the combination of complementary, redundant features will contribute to reduce the false alarm rate, improve the confidence in the target identification and the quality of the scene description as a whole. The combination of information provided by each feature extractor is performed according to the theory of evidence (Dempster–Shafer) in the third module. This paper presents an overview of the current functionality of IDFS. First results of evidential fusion are shown for land use mapping. Data sets were acquired over Indian-Head (Saskatchewan) with an airborne C-band polarimetric SAR and HSI Probe-1 sensors and were provided by the Canada Center for Remote Sensing.

Evaluation of C-band SAR data for wetlands mapping

This publication reports results of an experiment carried out to examine the potential of polarimetric C-band Synthetic Aperture Radar (SAR) for mapping various wetland classes found in the Mer Bleue region (near Ottawa, Canada). The Mer Bleue region was surveyed by the C-band (5.3 GHz) polarimetric (HH, HV, VH, VV) SAR of the Canada Centre for Remote Sensing (CCRS) at three times within the vegetation season: 16 June (spring flush for vegetation), 6 July (mature growth stage for vegetation) and 3 October 1995 (senescence). Signatures of six different cover types (forested and nonforested peat bog, marsh, open water, clearing and forests) have been derived as a function of incidence angle. Separability between various classes was used to determine the relationships between season(s) and polarization(s) needed to differentiate various wetland classes. A supervised classification was used for wetlands mapping by means of multipolarization data. These investigations demonstrate some of the capabilities of SAR at C-band for mapping wetlands. The cross-polarization data provided the best separation between the observed classes. The October dataset was better suited for discriminating between the classes present than the other periods observed. The overall accuracies of the classification are 73% for June, 73% for July and 86% for October. Classification using a single polarization has been investigated and the results have shown that the HH and cross-polarizations are better than VV polarization. For October, the percentage of all pixels correctly classified is 74% for HH polarization, 76% for cross-polarization, and 59% for VV polarization. Investigations were carried out to determine whether temporal changes can be used to increase the information content of single polarization C-band SAR data, which are now available from ERS-2 and RADARSAT satellites. They demonstrated that the use of multitemporal data acquired in June, July and October do not provide a substantial amelioration of the classification of wetlands when the differentiation is not possible in any single period.

Multitemporal C- and L-band polarimetric signatures of crops

Polarimetric synthetic aperture radar (SAR) data of agricultural fields have been acquired by the Danish L- and C-band polarimetric SAR (EMISAR) in March, May, June, and July of 1995, covering the agricultural test site at the Research Centre Foulum located in Central Jutland, Denmark. Polarimetric signatures for a number of agricultural crops, including both spring and winter crops, have been analyzed, both with respect to incidence angle variations, between-field differences, and multitemporal variations for the individual crops. The variation with incidence angle was most pronounced for the early acquisitions (March and May) where the backscatter was dominated by surface scattering. In later acquisitions (June and July), the variation was relatively small except for a few cases. Also, the largest between-field variation was observed for the early acquisitions, probably due to the sensitivity of the polarimetric parameters to differences in plowing and planting direction, soil texture, tillage practice, time of planting, etc. The analysis of the multitemporal signatures revealed significant information about the scattering mechanisms. Especially, the correlation coefficient between HH and VV showed a large difference between spring and winter crops in the beginning of the growing season, because of the dominance of surface scattering for spring crops and volume scattering for winter crops.

Mapping deforestation and land use in amazon rainforest by using SIR-C imagery

In this paper, the potential use of spaceborne polarimetric synthetic aperture radar (SAR) data in mapping landcover types and monitoring deforestation in tropics is studied. Here, the emphasis is placed on several clearing practices and forest regeneration that can be characterized by using the sensitivity of SAR channels to vegetation biomass and canopy structure. A supervised Bayesian classifier designed for SAR signal statistics is employed to separate five classes: primary forest, secondary forest, pasture-crops, quebradao, and disturbed forest. The L- and C-band polarimetric SAR data acquired during the shuttle imaging radar-C (SIR-C)/X-SAR space-shuttle mission in 1994 are used as input data to the classifier. The results are verified by field observation and comparison with the Landsat data acquired in August of 1994. The SAR data can delineate these five classes with approximately 72% accuracy. The confusion arises when separating old secondary forests from primary forest and the young ones from pasture-crops. It is shown that Landsat and SAR data carry complementary information about the vegetation structure that, when used in synergism, may increase the classification accuracy over secondary forest regrowth. When the number of land-cover types was reduced to three classes including primary forest, pasture-crops, and regrowth-disturbed forest, the accuracy of classification increased to 87%. A dimensionality analysis of the classifier showed that the accuracy can be further improved to 92% by reducing the feature space to L-band HH and HV channels. Comparison of SIR-C data acquired in April (wet period) and October (dry period) indicates that multi-temporal data can be used for monitoring deforestation; however, the data acquired during the wet season are not suitable for accurate land-cover classification.

Inferring snow wetness using C-band data from SIR-C's polarimetric synthetic aperture radar

In hydrological investigations, modeling and forecasting of snow melt runoff require timely information about spatial variability of snow properties, among them the liquid water content-snow wetness-in the top layer of a snow pack. The authors' polarimetric model shows that scattering mechanisms control the relationship between snow wetness and the copolarization signals in data from a multi-parameter synthetic aperture radar. Along with snow wetness, the surface roughness and local incidence angle also affect the copolarization signals, making them either larger or smaller depending on the snow parameters, surface roughness, and incidence angle. The authors base their algorithm for retrieving snow wetness from SIR-C/X-SAR on a first-order scattering model that includes both surface and volume scattering. It is applicable for incidence angles from 25/spl deg/-70/spl deg/ and for surface roughness with rms height /spl les/7 mm and correlation length /spl les/25 cm. Comparison with ground measurements showed that the absolute error in snow wetness inferred from the imagery was within 2.5% at 95% confidence interval. Typically the free liquid water content of snow ranges from 0% to 15% by volume. The authors conclude that a C-band polarimetric SAR can provide useful estimates of the wetness of the top layers of seasonal snow packs.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Crop classification from C-band polarimetric radar data

Abstract Synthetic aperture radar “SAR” systems are an attractive source of information for agricultural crop classification applications, particularly in regions where cloud cover is a problem. The accuracy with which crops can be classified is dependent on a range of sensor properties, including the SAR operating configuration. This paper focuses on the effect of one aspect of the SAR operating configuration, polarization, on crop classification accuracy using uncalibrated C-band polarimetric SAR data. Conventional like- and cro spolarized configurations and polarimetric coefficients “the pedestal and variation coefficients” were used as discriminating variables in classifications of agricultural crops. Two approaches to classification were investigated, a discriminant analysis and an artif cial neural network and results from a set of training classifications are presented. The results show that the polarimetric coefficients used provided a high level of inter-class discrimination and that a nine-class...

Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution

Abstract Multi-look polarimetric SAR (synthetic aperture radar) data can be represented either in Mueller matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution. This algorithm can also be applied to multifrequency multi-look polarimetric SAR data, as well as 10 SAR data containing only intensity information. A procedure is then developed for unsupervised classification. The classification error is assessed by using Monte Carlo simulation of multilook polarimetric SAR data, owing to the lack of ground truth for each pixel. Comparisons of classification errors using the training sets and single-look data are also made. Applications of this algorithm are demonstrated with NASA/JPL P-, L- and C-band polarimetric SAR data.

Knowledge-based classification of polarimetric SAR images

In preparation for the flight of the Shuttle Imaging Radar-C (SIR-C) on board the Space Shuttle in the spring of 1994, a level-1 automatic classifier was developed on the basis of polarimetric SAR images acquired by the JPL AirSAR system. The classifier uses L- and C-Band polarimetric SAR measurements of the imaged scene to classify individual pixels into one of four categories: tall vegetation (trees), short vegetation, urban, or bare surface, with the last category encompassing water surfaces, bare soil surfaces, and concrete or asphalt-covered surfaces. The classifier design uses knowledge of the nature of radar backscattering from surfaces and volumes to construct appropriate discriminators in a sequential format. The classifier, which was developed using training areas in a test site in Northern Michigan, was tested against independent test areas in the same test site and in another site imaged three months earlier. Among all cases and all categories, the classification accuracy ranged between 91% and 100%.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>