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A two-branch residual deep learning network is proposed for polarimetric synthetic aperture radar (PolSAR) image classification in this work. The proposed method integrates local information of pix...

A random patches-based edge-preserving network (RPEP) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification in this paper. An initial spatial feature extraction is fir...

Existing deep learning-based change detection methods in the field of polarimetric synthetic aperture radar (POLSAR) usually directly deal with intensity images. Methods can be easily transferred f...

Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR)...

The data classification of fully polarimetric synthetic aperture radar (PolSAR) is one of the favourite topics in the remote sensing community. To date, a wide variety of algorithms have been utilized for PolSAR data classification, and among them kernel methods are the most attractive algorithms for this purpose. The most famous kernel method, i.e., the support vector machine (SVM) has been widely used for PolSAR data classification. However, until now, no studies to classify PolSAR data have been carried out using certain extended SVM versions, such as the least squares support vector machine (LSSVM), relevance vector machine (RVM) and import vector machine (IVM). Therefore, this work has employed and compared these four kernel methods for the classification of three PolSAR data sets. These methods were compared in two groups: the SVM and LSSVM as non-probabilistic kernel methods vs. the RVM and IVM as probabilistic kernel methods. In general, the results demonstrated that the SVM was marginally better, more accurate and more stable than the probabilistic kernels. Furthermore, the LSSVM performed much faster than the probabilistic kernel methods and its associated version, the SVM, with comparable accuracy.

As China’s first civil synthetic aperture radar (SAR) satellite, the data quality of the GaoFen-3 (GF-3) satellite has been widely studied. This study analyzes the data quality of this satellite fr...

Polarimetric Synthetic Aperture Radar (PolSAR) is one of the most important remote sensing tools. However, PolSAR images are strongly contaminated by a multidimensional interference (called speckle...

International audience; It is a challenge to develop methods which can process the PolSAR and multispectral (MS) data modalities together without losing information from either for remote sensing applications. This paper presents a study which attempts to introduce novel deep learning based remote sensing data processing frameworks that utilizes convolutional neural networks (CNNs) in both spatial and spectral domains to perform land cover (LC) classification with PolSAR-MS data. Also since earth observation remotely sensed data have usually larger spectral depth than normal camera image data, exploiting the spectral information in remote sensing (RS) data is crucial as well. In fact, convolutions in the sub-spectral space are intuitive and alternative to the process of feature selection. Recently, researchers have gained success in exploiting the spectral information of RS data, especially the hyperspectral data with CNNs. In this paper, exploitation of the spectral information in the PolSAR-MS data via a permuted localized spectral convolution along with localized spatial convolution is proposed. Further, the study in this paper also establishes the significance of performing permuted localized spectral convolutions over non-localized or localized spectral convolutions. Two models are proposed, namely a permuted local spectral convolutional network (Perm-LS-CNN) and a permuted local spectral-spatial convolutional network (Perm-LSS-CNN). These models are trained on ground truth class data points measured directly on the terrain. The evaluation of the generalization performance is done using ground truth knowledge on selected well known regions in the study areas. Comparison with other popular machine learning classifiers shows that the Perm-LSS-CNN model provides better classification results in terms of both accuracy and generalization.

Despite their natural and societal importance, wetlands are becoming increasingly threatened. The goal of this study is to investigate the potential of polarimetric synthetic aperture radar (SAR) f...

Polarimetric Synthetic Aperture Radar (PolSAR) imaging extended SAR applications by exploring the polarimetric properties of the target scatterers. Similar to SAR images, PolSAR images are prone to multiplicative speckle noise due to its coherent imaging mechanism. Hence, despeckling is an essential procedure for effectively utilizing the PolSAR images for remote sensing applications. Latest advances in PolSAR filtering techniques have shown the trend of using spatial domain techniques based on multiplicative speckle noise model which impairs the retrieved image by oversmoothing and absence of image finer details. More efficient despeckling algorithm using bandelet thresholding is proposed in this paper. Since the bandelet transform is computationally expensive, a spatially adaptive technique using directional variance is proposed. Performance evaluation with both airborne and spaceborne radar images has shown the approached technique is much efficient related to speckle reduction and feature preservation compared to the state of art techniques.

Advancement in remote sensing technology and microwave sensors explores the applications of remote sensing in different fields. Microwave remote sensing encompasses its benefits of providing cloud-...

This paper examines a simple geometrical method for forest height estimation using single-baseline single frequency polarimetric synthetic aperture radar interferometry (PolInSAR) data. The suggest...

The overestimation of volume scattering (OVS) is an essential shortcoming of the model-based polarimetric synthetic aperture radar (PolSAR) target decomposition method. It is likely to affe...

The analysis of hybrid-polarimetric synthetic aperture radar (hybrid-pol SAR) data can be carried out using two different types of analytical approaches. The first approach is by implementing direc...

The ship detection in polarimetric synthetic aperture radar (PolSAR) mode is a hot topic in recent years, because of the diversity of polarimetric scattering mechanisms between ship targets and sea...

Combining optical and polarimetric synthetic aperture radar (PolSAR) earth observations offers a complementary data set with a significant number of spectral, textural, and polarimetric features fo...

A novel unsupervised image classification algorithm which based on the sparse representation theory for polarimetric synthetic aperture radar (PolSAR) image is introduced in this paper. The algorit...

The present study introduces distance based change detection (CD) algorithms in polarimetric synthetic aperture radar (PolSAR) data. PolSAR images, due to interactions between electromagnetic waves...

Polarimetric Synthetic Aperture Radar (PolSAR) images are an important source of information. Speckle noise gives SAR images a granular appearance that makes interpretation and analysis hard tasks. A major issue is the assessment of information content in these kind of images, and how it is affected by usual processing techniques. Previous works have resulted in various approaches for quantifying image information content. As Narayanan, Desetty, and Reichenbach(2002) we study this problem from the classification accuracy viewpoint, focusing in the filtering and the classification stages. Thus, through classified images we verify how changing properties of the input data affects their quality. Our input is an actual PolSAR image, the control parameters are the filter (Local Mean or Model Based PolSAR, MBPolSAR), the size of them and the classification method (Maximum Likelihood, ML, or Support Vector Machine, SVM), and the output are the classification precision obtained applying the classification algorithm to the filtered data. To expand the conclusions, this study deals not only with Classification Accuracy, but also with Kappa and Overall Accuracy as measures of map precision. Experiments were conducted on two airborne PolSAR images. Unless Narayanan, Desetty, and Reichenbach(2002) almost all measure values are good and increase with degradation, i.e. the filtering algorithm that we used always improves the classification results at least up to 7x7., Comment: 16 pages, 9 figures, Accepted for publication in the International Journal of Remote Sensing

In this article, we evaluate a series of POLSAR (Polarimetric Synthetic Aperture Radar) parameters and devise a robust, multi-date, and hierarchical decision tree algorithm for crop discrimination....

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. Thi...

Agriculture is an important sector in Canada, and annual crop inventories are required in many agricultural applications. Multi-temporal polarimetric synthetic aperture radar (SAR) data have great potential in crop classification due to its less dependency on weather condition. This study, for the first time, investigated the effects of the Minimum Noise Fraction (MNF) transformation of multi-temporal RADARSAT-2 polarimetric SAR data on the performance of cropland classification through the discussing of the performance of different polarimetric SAR parameter sets, and the impact of the timing of RADARSAT-2 datasets in southwestern Ontario. The random forest classifier was adopted due to its excellent ability in crop classification. The results illustrated that the elements of coherency matrix performed the best in agricultural land cover classification. The multi-temporal polarimetric SAR data acquired from the end of June to November gave the best classification accuracy, and an overall accuracy...

Automatic edge detection for polarimetric synthetic aperture radar (PolSAR) images plays a fundamental role in various PolSAR applications. The classic methods apply the fixed-shape windows to dete...

This focused study aimed to generate a fully polarimetric synthetic aperture radar (SAR) data set of 1 m resolution based on the spotlight and stripmap COSMO-SkyMed (CSK) satellite data fusion. The...

Cropland classification using optical and full polarimetric synthetic aperture radar (PolSAR) images is a topic of considerable interest in the remote-sensing community. These two data sources can ...

Increasing studies have been conducted to investigate the potential of polarimetric synthetic aperture radar (SAR) in crop growth monitoring due to the capability of penetrating the clouds, haze, light rain, and vegetation canopy. This study investigated the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) of corn and wheat. The in-situ measured crop height and FVC were collected from 29 April to 30 September 2013, at the study site in southwest Ontario, Canada. A total of 10 Radarsat-2 polarimetric SAR images were acquired throughout the same growing season. It was observed that at the early growing stage, the corn height was strongly correlated with the SAR parameters including HV (R2 = 0.88), HH-VV (R2 = 0.84), and HV/VV (R2 = 0.80), and the corn FVC was significantly correlated with HV (R2 = 0.79) and HV/VV (R2 = 0.92), but the correlation became weaker at the later growing stage. The sensitivity of the SAR p...

In this study, an analysis of the polarimetric synthetic aperture radar (SAR) capabilities to classify coastal areas is undertaken. The Yellow River delta (China) is selected as the test case since it represents an extraordinary environmental and economical area, which is characterized by a very heterogeneous scattering scenario, as witnessed by official reference data, provided by the Chinese government, that classified 12 different kinds of environment. Experimental results, obtained applying two well-known unsupervised classifiers, namely the H/α-based and the Freeman–Durden model-based algorithms, to a fully polarimetric SAR scene collected by Radarsat-2 in 2008 are compared and critically discussed. Both provide a satisfactory global accuracy (larger than 60% in average) with reference to the inland Yellow River delta area, but there are subareas that result in misclassifications and severe classification ambiguities. This study also suggests including single-polarization intensity informatio...

Analysis of hybrid polarimetric synthetic aperture radar data has gained importance in the last couple of years with the availability of spaceborne data from Radar Imaging Satellite-1 (RISAT-1). RISAT-1 provides right circular transmit and linear receive data in Fine Resolution Stripmap-1 (FRS-1) mode with a swath of 25 km approximately covering 625 km2 areas. But an administrative unit, like a district, in India cannot be covered in single FRS-1 acquisition. In this article, the possibility of acquisition of multi-incidence angle FRS-1 data to cover a larger area in three consecutive days over Khagaria district of Bihar State, India, for maize crop discrimination and mapping was investigated. It was assumed that the difference of 3 days in imaging does not affect the backscatter response from maize crop as there will not be much change in the maize crop characteristics in 3 days. The backscatter response of maize crop, which is in maximum vegetative stage, was studied at three incidence angles (v...

This article investigates the scattering characteristics of ridging patterns in agriculture by the use of C-band polarimetric synthetic aperture radar PolSAR images. The polarimetric signatures of periodic potato fields and row wheat in different directions are highlighted using a set of polarimetric parameters. Enhanced coherent scattering is observed when the alignment direction of the ridging patterns is perpendicular to the radar’s line of sight LOS. The dominant backscattering mechanism of the ridging patterns is deduced by evaluating different polarimetric parameters. The increased copolarized backscattering coefficients and copolarized correlation coefficient, and the reduced entropy and polarimetric alpha angle, indicate a strong contribution of odd scattering to ridging patterns aligned perpendicular to the LOS. We also compare the dominant contributions to the backscattering of ridging patterns in different phenological stages. Although the canopy changes of potato and wheat with time are significant, the underlying periodic surface changes the dominant scattering mechanism of potato fields over all the phenological stages, and the wheat aligned parallel with the flight direction of radar still has relatively high coherent scattering in the different vegetation development stages. The variability analyses undertake in this study allow a more detailed documentation of the physical scattering process of the ridging patterns in agriculture, and will improve the applicability of synthetic aperture radar images in agriculture.

Deorientation plays an important role in polarimetric synthetic aperture radar (PolSAR) target decomposition, terrain classification, and geophysical parameters retrieval. The existing deorientation method roughly rotates the target by an average/mixed orientation angle (OA) about the line of radar sight. However, regarding the complex high-entropy mixed scatterer, which usually comprises several comparable sub-scatterers with different OAs, the average/mixed OA is obviously insufficient to account for the diverse OAs’ reality. To address this, a novel PolSAR data deorientation method is proposed in this letter. The proposed method deorients a mixed scatterer by reconstructing the underlying sub-scatterers using the eigenvalue-based Cloude–Pottier decomposition first, and then compensates the OA of each reconstructed sub-scatterer using Huynen’s desying operation, respectively. One important feature of the proposed method is that it is consistent with Huynen’s desying operation that the real part ...

Building extraction from remote sensing images is very important in many fields, such as urban planning, land use investigation, damage assessment, and so on. In polarimetric synthetic aperture radar (PolSAR) imagery, the buildings not only have typical polarimetric features but also have rich texture features. In this paper, the texture information is introduced to improve the accuracy of urban building extraction from PolSAR imagery by a new method called cross reclassification. Based on this method, the polarimetric information-based results and texture-based results can be effectively fused. The experimental results of three representative PolSAR images with different characteristics demonstrate the effectiveness of the proposed method, and the accuracy of building extraction can be improved, compared with the traditional method using only polarimetric information.

Compact polarimetric synthetic aperture radar (SAR) architecture is an SAR configuration that consists of transmitting a single circular polarization (left or right) or a 45° oriented linear signal while receiving two linear polarizations, horizontal and vertical. In this study we investigate the potential of the compact polarimetric SAR mode for wetland monitoring applications. Whitewater Lake located in Manitoba, Canada, is selected as a case study where simulated compact polarimetric SAR data are obtained using RADARSAT-2 Fine Quad-POL SAR images. The ability of the compact polarimetric data to monitor wetlands using the Wishart-Chernoff distance is studied and compared to the results obtained using fully polarimetric data. Results of this study show that compact polarimetry provides monitoring capabilities for wetlands. Promising change detection mapping results based on the compact polarimetric coherency matrices are obtained using the Wishart-Chernoff distance. This could be useful for fla...

Forest aboveground biomass (AGB) is essential for monitoring the carbon cycle budget and climate change. This study proposes a method for the estimation of forest AGB based on polarization coherence tomography (PCT) and polarimetric synthetic aperture radar (PolSAR) segmentation. The data used are the single-baseline polarimetric SAR interferometry data acquired by the German Aerospace Center’s E-SAR sensor at the L-band over the city of Traunstein and its vicinity in 2003. First, vertical structure tomographic profiles (relative reflectivity distribution in vertical direction) were produced by PCT for each pixel and then averaged within each field-surveyed forest stand to obtain the mean tomographic profile. Next, the mean vertical tomographic profiles were parameterized by 10 shape parameters. Several models for biomass estimation were designed based on the relationships between the definition parameters and the in situ measurements using backward step-wise regression and the M-fold (10-fold) cross-vali...

Ship detection can be significantly improved by using polarimetric synthetic aperture radar (PolSAR) imaging. In this article, we propose a PolSAR ship detection method based on the use of multi-featured polarization by using the visual attention model. Three polarimetric features, namely, the polarimetric contrast, the polarimetric scattering, and the polarimetric phase, are selected as the early features, and the pros and cons for each feature are discussed. The visual attention model is a framework that rapidly combines multiple features into one feature, which is improved according to the relationship of the selected features. Validation of the method is performed by analysing the multi-resolution process, the improved multi-feature process, the threshold strategy, the sensibility to the incidence angle of the sensors, and the performance of moving ship detection, which are analysed by Radarsat-2 fine quad images with automatic identification system data. Additionally, the false alarm/non-detection an...

This letter presents a novel semi-supervised method based on hypergraph learning for polarimetric synthetic aperture radar (PolSAR) image classification. Compared with the classic support vector machine, simple-graph learning, k-nearest neighbour (k-NN) and semi-supervised discriminant analysis (SDA) classifiers, the proposed method achieves better performance with fewer labelled points for PolSAR imagery. A hyperspectral image is used for comparison with use of PolSAR imagery, and the proposed method is found to be inferior to k-NN and SDA for the hyperspectral image. The performance of our method is evaluated in single, dual and full-polarization cases, respectively. The results demonstrate that the performance of our method in the full-polarization case is superior to that in either single or dual-polarization case.

A new procedure is proposed to find the optimum polarizations for the case of unconstrained relationship between the emitted and received polarizations of radar antennas. Similar to the three-step procedure, the optimum polarizations are found by adjusting the polarizations of radar antennas to result in maximum or minimum received power. But the proposed procedure is different from the three-step procedure, in which the proposed one can be applied to both the coherent and the incoherent scattering cases. The transmitted optimal polarization states are obtained by optimizing the polarimetric power received under the conditions of antenna mismatched/matched receiving for the partially polarized waves. Real polarimetric synthetic aperture radar (SAR) data are used to compare the proposed procedure with the currently available procedures. The results indicate that the proposed procedure is validated to be efficient in calculating the optimal polarization states of the receiving and transmitting antenna.

In recent years, the use of synthetic aperture radar (SAR) for iceberg detection has been increasing thanks to the greater availability and coverage of SAR data, particularly polarimetric data. Greater amounts of polarimetric information can increase detection performance, preventing false alarms and missed detections. However, quad-polarization (quad-pol) SAR systems have increased data rate and power usage requirements, causing quad-pol modes to have generally half the swath width of dual-polarization (dual-pol) modes. Compact polarimetry is a compromise that allows the approximation of quad-pol information (referred to as “pseudo quad-pol”) using a dual-pol SAR. In this paper, using compact polarimetric data simulated from RADARSAT-2 quad-pol imagery, we show how pseudo-quad-pol data can be used for iceberg detection using the likelihood ratio test method. We show that use of the pseudo-quad-pol HV intensity can augment the detection performance of a compact polarimetric SAR system. We also compare the...

The leaf area index (LAI) is an important parameter to quantitatively describe the structure of vegetation and crops. Uncertainty in the relationship between the LAI and polarimetric parameters is the key problem for LAI estimation from polarimetric synthetic aperture radar (POLSAR) data. However, the existing POLSAR data have difficulties meeting the demand of the aforementioned research. This paper analyses the correlations between the LAI and the polarimetric parameters derived from Cloude and Freeman decompositions using simulated POLSAR data based on a coherent scattering model for maize and wheat. The results show: (1) The POLSAR data at C-band with a large incidence angle (40 degrees) are very suitable for finding the LAI for maize and wheat. (2) For maize there is a strong correlation between the scattering type angle and the LAI at C-band with a large incidence angle, and the coherency entropy, anisotropy, and the power of the double-bounce scattering power component also have significan...

With the current pace of regional and global urbanization, change detection and monitoring of urban areas using multitemporal synthetic aperture radar (SAR) datasets is becoming an important research topic for SAR Earth observation. Many SAR change detection methods have been proposed, but the change detection methods based on full polarimetric SAR and polarimetric SAR interferometry have not been extensively studied. In addition, due to the complexity of urban environments, most of the change detection methods were focused on natural targets. In this study, three urban area change detection methods are proposed using multitemporal RADARSAT-2 polarimetric SAR datasets; the Stationary Index Method (SIM) of urban area change detection using polarimetric SAR interferometry, the Coherence Index Method (CIM) of urban area change detection using polarimetric SAR interferometry, and the Two Scattering Components Method (TSCM) of manmade target change detection using polarimetric SAR. Three methods were applied t...

The polarimetric synthetic aperture radar (PolSAR) is becoming more and more popular in remote-sensing research areas. However, due to system limitations, such as bandwidth of the signal and the physical dimension of antennas, the resolution of PolSAR images cannot be compared with those of optical remote-sensing images. Super-resolution processing of PolSAR images is usually desired for PolSAR image applications, such as image interpretation and target detection. Usually, in a PolSAR image, each resolution contains several different scattering mechanisms. If these mechanisms can be allocated to different parts within one resolution cell, details of the images can be enhanced, which that means the resolution of the images is improved. In this article, a novel super-resolution algorithm for PolSAR images is proposed, in which polarimetric target decomposition and polarimetric spatial correlation are both taken into consideration. The super-resolution method, based on polarimetric spatial correlation (SRPSC...

Rice is one of the three largest food grains in the world. Radar remote sensing has been proven to be an effective tool for rice monitoring. With the emergence of spaceborne polarimetric synthetic aperture radar (SAR) satellites, research on polarization backscatter behaviors and identification methods for rice are of great significance and attract the attention of remote sensing communities. The Zhazuo area located in Guizhou province of southwest China was selected as the test site for this study. The RADARSAT-2 polarimetric data acquired on four different dates were used to analyze the polarization backscatter behaviors and temporal variation of rice. Identification methods for rice based on polarization combinations and polarization decompositions were developed. The results indicated that HH with HH/HV was the optimal polarization combination for rice identification, with an accuracy of up to 86.65%. Based on the Pauli decomposition, rice can be discriminated effectively, with an accuracy of about 87...

An extended multiple-component scattering model (MCSM) is proposed for polarimetric synthetic aperture radar (PolSAR) image decomposition. The MCSM is an extension of the three-component scattering model (TCSM), and it describes single-bounce, double-bounce, volume, helix and wire scattering as elementary scattering mechanisms in the analysis of PolSAR images. The proposed MCSM is demonstrated with German Aerospace Centre (DLR) experimental SAR (ESAR) L-band fully polarized images of the Oberpfaffenhofen Test Site Area (DE), Germany. Double-bounce, helix and wire scattering are found to be predominant in urban areas and the results confirm that the MCSM is effective for analysis of buildings in urban areas. A comparison of the TCSM and its extended models is also implemented.

Classification of the Earth's surface constitutes an important application of polarimetric synthetic aperture radar (SAR) data; in turn, it may represent an efficient way for investigating their different representations. The polarimetric parameters most frequently taken into account for classification have been the incoherent ones. A similar use of coherent methods appears to have been scarcely considered and remained neglected. In this contribution, we wish to address this issue, testing and comparing a wide range of polarimetric SAR parameters, coherent and incoherent. Another original aspect of this work is the study of the dependence of the classification results on the varying size of averaging windows of pixels. Such an analysis will permit us to evaluate the importance of speckle reduction and to prove if the chosen polarimetric parameters describe only point-like physical properties of the targets or if they also contain 'extended', local information. The goal is to provide an objective estimate of the quality of the classification of polarimetric parameters and afford their comparison, an exercise hitherto unavailable in the literature in common knowledge.

The ship-detection performance that can be obtained from polarimetric synthetic aperture radar (SAR) data is compared with that obtained from single-channel SAR data. Statistical decision theory is used to define decision variables that quantify the tradeoff between the probability of missed detection and the probability of false alarm; performance is characterized by calculating receiver operating characteristics from single-channel and polarimetric SAR data by using likelihood ratio tests with the Neyman–Pearson criterion. It is shown that ship-detection performance obtained with polarimetric SAR data is improved compared with that obtained with single-channel SAR data. We also evaluate the results of these algorithms when applied to single-channel, dual-channel amplitude-only, dual-channel with amplitude and phase, and fully polarimetric SAR data of known ships. In this way, the relative improvement in ship-detection performance that is realized by using polarimetric information is quantified.

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.

This study provides an update of the polarimetric tools currently being used for optimum information extraction from polarimetric synthetic aperture radar (SAR) images. The basics of polarimetric theory are summarized and discussed in the context of SAR. Calibration of polarimetric SAR, which is an important issue for the extraction of meaningful polarization information, is reviewed. Information extraction using the scattered and received wave parameters and target decomposition theory is considered. In particular, the use of coherent versus incoherent target decomposition is discussed and the practical limitations of these target decompositions are outlined. Speckle filtering and classification of polarimetric SAR images are also thoroughly analyzed, and the important directions for future research are outlined.

In this paper, by using the joint probability density functions (PDFs) of the scattering amplitude functions, the PDFs of four Stokes parameters are derived for multi-look polarimetric synthetic aperture radar (SAR) data. The PDFs are formulated analytically with dependence on the look number and mean values of the scattered Stokes parameters. The statistics histograms of four Stokes parameters are validated by the AirSAR imagery at multi-P, L, and C bands observed over some typical sites such as water, sparse trees, and forest. An effective look number might be specially assigned to provide an excellent match with the AirSAR data. Potential application of the statistics of four Stokes parameters to classification of the terrain types is studied.

A new remote sensing technique using polarimetric synthetic aperture radar (SAR) data has been developed which can measure terrain slopes in the azimuthal, or along-track, direction. Terrain elevation maps can then be generated by integrating these slopes. The processing of both single-pass, and orthogonal two-pass, datasets is investigated. When single-pass SAR data is used elevation groundtruth must be available for at least one point of each profile formed in the azimuthal direction. When orthogonal two-pass slope data is employed, the elevation surface may be generated as an iterative solution of the Poisson equation and only a single elevation tie-point is required. The results of two studies are presented. In the first study slopes have been measured using single-pass P-band data for a forested area in northern California. The elevation surface generated with this data has been compared with a Digital Elevation Model (DEM) produced by a C-band interferometric SAR. The second study uses orthogonal tw...

The goal of this research was to decompose polarimetric Synthetic Aperture Radar (SAR) imagery of upland and flooded forests into three backscatter types: single reflection, double reflection, and cross-polarized backscatter. We used a decomposition method that exploits the covariance matrix of backscatter terms. First we applied this method to SAR imagery of dihedral and trihedral corner reflectors positioned on a smooth, dry lake bed, and verified that it accurately isolated the different backscatter types. We then applied the method to decompose multi-frequency Jet Propulsion Laboratory (JPL) airborne SAR (AIRSAR) backscatter from upland and flooded forests to explain scattering components in SAR imagery from forested surfaces. For upland ponderosa pine forest in California, as SAR wavelength increased from C-band to P-band, scattering with an odd number of reflections decreased and scattering with an even number of reflections increased. There was no obvious trend with wavelength for cross-po...

The Joint Research Centre “JRC” Ispra of the European Commission “EC” and the European Space Agency “ESA” supported and financed the deployment of the National Aeronautics and Space Administration “NASA” and the Jet Propulsion Laboratory “JPL” Co, L- and P-band polarimetric synthetic aperture radar “SAR” in Europe in mid-August 1989. The campaign was entitled MAESTRO I. This deployment represented the first opportunity for European agencies and institutes to evaluate the current state of the art of multi-frequency polarimetric imaging technology over European test sites. In addition, the campaign proved to be an excellent test for the European initiative “a joint EC/ESA CEC/ESA project” to build a European suite of sensors, entitled European Airborne Remote Sensing Capabilities “EARSEC”. This paper provides the overall background and description of the MAESTRO I Campaign.