Multifrequency Polarimetric SAR Research Articles

Exploring optimal combinations of multi-frequency polarimetric SAR observations to estimate forest above-ground biomass

ABSTRACT The penetration capability of electromagnetic wave signal into forest increases with increasing wavelength. SAR data at each frequency senses different components of forest structure. Therefore the biomass distributed at various tree components could be estimated using different radar frequencies. Additionally, the synthesis of multiple SAR frequencies could improve the accuracy in retrieving forest above-ground biomass (AGB). Taking advantage of available X-, C-, L-, and P-band quad-polarimetric SAR images of airborne or spaceborne for the test site located at Genhe national forest scientific field station, we used a Genetic Algorithm and Support Vector Regression optimization algorithm (GA-SVR) to explore the sensitivity of polarimetric observations at various frequencies to forest AGB and effectiveness of AGB retrievals using single-frequency, dual-frequency, triple-frequency, and quad-frequency SAR observation combinations. We found that: (i) Most of the polarimetric observations are sensitive to forest AGB, (ii) GA-SVR performed well in forest AGB retrieval using the single frequency SAR observations or combinations of multi-frequency observations; the highest Acc. value for single-frequency-retrieved results is 75.13% acquired at P-band, with multi-frequency, the highest Acc. values is 77.34% acquired by combining C- and P-band. (iii) For forest AGB retrievals, the single-frequency P-band accuracy is comparable to the combined C- and P-band retrieval accuracy, indicating that the long-wavelength single-frequency P-band is sufficient for an accurate forest AGB retrieval. The findings reconfirmed potential of P-band for forest AGB retrievals, they also demonstrated that the optimal combination of multi-frequency SAR for AGB retrievals is by using a short-wavelength (X/C-) and a long-wavelength (L/P-).

Multifrequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure

Compared with the rapid development of single-frequency polarimetric SAR (PolSAR) image classification technology, there is less research on the land cover classification of multi-frequency PolSAR (MF-PolSAR) images. And the deep learning methods among them are mainly based on convolutional neural networks (CNNs), only local spatiality is considered but the nonlocal relationship is ignored. Therefore, this paper proposes the MF semantics and topology fusion (MF-STF) model based on semantic interaction and nonlocal topological structure to improve MF-PolSAR classification performance. During MF-STF optimization, the semantic information-based classification (SIC) and topological property-based classification (TPC) work collaboratively, not only fully leveraging the complementarity of bands, but also combining local and nonlocal spatial information to improve the discrimination of different categories. For SIC, the designed cross-band interactive feature extraction (CIFE) module is embedded to explicitly model the deep semantic correlation among bands, thereby leveraging the complementarity of bands to make ground objects more separable. In TPC, the graph sample and aggregate network (GraphSAGE) is employed to dynamically capture the representation of nonlocal topological relations between land cover categories. In this way, the robustness of classification can be further improved by combining nonlocal spatial information. Finally, a MF weighted fusion (MFWF) strategy is proposed to merge inference from different bands, so as to make the MF joint classification decisions of SIC and TPC. Notably, its weights are adjusted based on the total model loss. The effectiveness of the proposed modules is proved by ablation experiments on three measured MF-PolSAR datasets. In addition, the comparative experiments show that MF-STF can achieve more competitive classification performance than some state-of-the-art methods.

Sensitivity of Multifrequency Polarimetric SAR Data to Postfire Permafrost Changes and Recovery Processes in Arctic Tundra

We used full-polarimetric L-band and P-band synthetic aperture radar (SAR) data collected from the recent NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign and Sentinel-1 C-band dual-polarization data to understand the sensitivity of radar backscatter intensity and phase to fire-induced changes in the surface and subsurface soil processes in Arctic tundra underlain by permafrost. The 2007 Anaktuvuk River fire on the Alaska North Slope was used as a case study. At ~10-year postfire, we observed a strong increase (>~3–4 dB) in the low-frequency radar backscatter in severely burned areas during the thaw season, in contrast to limited (< ~0.5 dB) C-band backscatter differences (VV and VH) between burned and unburned areas. However, C-band winter backscatter is generally higher (>1 dB) in burned areas than the adjacent unburned areas. Polarimetric decomposition analysis indicated a general trend toward more random surface scattering, and strong increases in double-bounce scattering and volume scattering power at both P- and L-band in the burned areas. The ice-rich yedoma region shows the largest backscatter increases in burned areas and the highest correlation with burn severity and microtopography changes. The above backscatter changes are attributed to increasing surface roughness and microtopography due to ice-wedge degradation and thermokarst development and increasing subsurface scattering due to an overall drier and deeper active layer in burned areas. Among all frequencies, P-band shows consistently larger contrast in backscatter power and phase between burned and unburned areas, which makes it potentially more useful to study fire–permafrost interactions in the Arctic over decadal time scales.

A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).

Online Semisupervised Active Classification for Multiview PolSAR Data.

Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and have multiple views obtained from different feature extractors or multiple frequency bands. The fast and accurate classification of PolSAR data in dynamically changing environments is a critical and challenging task. Online learning can handle this task by learning a classifier incrementally from a stream of samples. In this article, we propose an online semisupervised active learning framework for multiview PolSAR data classification, called OSAM. First, a novel online active learning strategy is designed based on the relationships among multiple views and a randomized rule, which allows to only query the labels of some informative incoming samples. Then, in order to utilize both the incoming labeled and unlabeled samples to update the classifiers, a novel online semisupervised learning model is proposed based on co-regularized multiview learning and graph regularization. In addition, the proposed method can deal with the dynamic large-scale multifeature or multifrequency PolSAR data where not only the amount of data but also the number of classes gradually increases in the learning process. Moreover, the mistake bound of the proposed method is derived rigorously. Extensive experiments are conducted on real PolSAR data to evaluate the performance of our algorithm, and the results demonstrate the effectiveness of the proposed method.

Study of Coniferous Forests Using Multifrequency Polarimetric Synthetic-Aperture Radar

Specific features of radio reflection of coniferous forest in decimeter- and meter-wavelength ranges are considered. The study is stimulated by the upcoming (in five-to-seven years) spaceborne experiments using synthetic-aperture radars (SARs) working in the P-band (430 MHz), which is important for sounding of forests. Experimental results on the reflection characteristics of pine forests obtained with the aid of multifrequency polarimetric SAR are presented. Reflection from forests is simulated for the meter-wavelength range for horizontal (HH) and vertical (VV) polarizations. It is shown that different effects may determine reflection of meter-wavelength waves with HH and VV polarizations. New results on specific features of airborne and spaceborne radar images (RIs) of forests in the L, P, and VHF bands are obtained. Typical features of the RIs of coniferous forests are revealed. Such features are difficult to interpret and need to be further studied using RI analysis and results of simultaneous measurements of soil and forest parameters. Several examples show that RI interpretations may cause doubts concerning the correctness.

Multifrequency Polarimetric SAR Image Despeckling by Iterative Nonlocal Means Based on a Space-Frequency Information Joint Covariance Matrix

This paper presents an iterative nonlocal means (NLM) filtering method under the Bayesian framework to deal with the issue of multifrequency fully polarimetric synthetic aperture radar (PolSAR) image despeckling. Differing from most of the PolSAR filters designed for single-frequency data, the proposed NLM method is developed based on a space-frequency information joint covariance matrix, which can not only utilize multifrequency polarimetric information but also exploit the correlation between any two pixels in an image patch. Furthermore, with the aim of accelerating the filtering procedure and better retaining image details, an effective preselection step is employed. The filtering results obtained with both a simulated dataset and real multifrequency PolSAR datasets acquired by the AIRSAR system confirm the good performance of the proposed method in both reducing speckle and retaining details, when compared with some of the state-of-the-art despeckling algorithms.

Optimized Wishart Network for an Efficient Classification of Multifrequency PolSAR Data

High-resolution wide-area images are required in the diverse field of research ranging from urban planning and disaster prediction to agriculture and geology. Sometimes the image is taken under harsh weather conditions or at night time. Current optical remote sensing technology does not have the capability to acquire images in such conditions. Synthetic aperture radar (SAR) uses microwave signal which has a long-range propagation characteristic that allows us to capture images in difficult weather conditions. In addition to this, some polarimetric SAR (PolSAR) systems are also capable of capturing images using multifrequency bands simultaneously resulting into a multitude of information in comparison to the optical images. In this letter, we propose a single-hidden layer optimized Wishart network (OWN) and extended OWN for classification of the single-frequency and multifrequancy PolSAR data, respectively. Performance evaluation is conducted on a single-frequency as well as multifrequency SAR data obtained by Airborne Synthetic Aperture Radar. We observed that for combining multiple band information, proposed single-hidden layer network outperforms deep learning-based architecture involving multiple hidden layers.

Novel supervised classification approach for multifrequency polarimetric SAR data

A novel method is proposed for the supervised classification of multifrequency polarimetric synthetic aperture radar(Pol SAR) images. The coherency matrices in P-, L-, and C-bands are mapped onto a 9×9 matrix Ω based on the eigenvalue decomposition of the coherency matrix of each band. A boxcar filter is then performed on the matrix Ω. The filtered data are put into a complex Wishart classifier. Finally, the effectiveness of the proposed method is demonstrated with JPL/AIRSAR multifrequency Pol SAR data acquired over the Flevoland area.

Classification of Multi-Frequency Polarimetric SAR Images Based on Multi-Linear Subspace Learning of Tensor Objects

One key problem for the classification of multi-frequency polarimetric SAR images is to extract target features simultaneously in the aspects of frequency, polarization and spatial texture. This paper proposes a new classification method for multi-frequency polarimetric SAR data based on tensor representation and multi-linear subspace learning (MLS). Firstly, each cell of the SAR images is represented by a third-order tensor in the frequency, polarization and spatial domains, with each order of tensor corresponding to one domain. Then, two main MLS methods, i.e., multi-linear principal component analysis (MPCA) and multi-linear extension of linear discriminant analysis (MLDA), are used to learn the third-order tensors. MPCA is used to analyze the principal component of the tensors. MLDA is applied to improve the discrimination between different land covers. Finally, the lower dimension subtensor features extracted by the MPCA and MLDA algorithms are classified with a neural network (NN) classifier. The classification scheme is accessed using multi-band polarimetric SAR images (C-, L- and P-band) acquired by the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) over the Flevoland area. Experimental results demonstrate that the proposed method has good classification performance in comparison with the classic multi-band Wishart classifier. The overall classification accuracy is close to 99%, even when the number of training samples is small.

Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

Normally, polarimetric SAR classification is a high-dimensional nonlinear mapping problem. In the realm of pattern recognition, sparse representation is a very efficacious and powerful approach. As classical descriptors of polarimetric SAR, covariance and coherency matrices are Hermitian semidefinite and form a Riemannian manifold. Conventional Euclidean metrics are not suitable for a Riemannian manifold, and hence, normal sparse representation classification cannot be applied to polarimetric SAR directly. This paper proposes a new land cover classification approach for polarimetric SAR. There are two principal novelties in this paper. First, a Stein kernel on a Riemannian manifold instead of Euclidean metrics, combined with sparse representation, is employed for polarimetric SAR land cover classification. This approach is named Stein-sparse representation-based classification (SRC). Second, using simultaneous sparse representation and reasonable assumptions of the correlation of representation among different frequency bands, Stein-SRC is generalized to simultaneous Stein-SRC for multi-frequency polarimetric SAR classification. These classifiers are assessed using polarimetric SAR images from the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) and the Electromagnetics Institute Synthetic Aperture Radar (EMISAR) sensor of the Technical University of Denmark (DTU). Experiments on single-band and multi-band data both show that these approaches acquire more accurate classification results in comparison to many conventional and advanced classifiers.

Sensitivity of Main Polarimetric Parameters of Multifrequency Polarimetric SAR Data to Soil Moisture and Surface Roughness Over Bare Agricultural Soils

The potential of polarimetric synthetic aperture radar data for the soil surface characterization of bare agricultural soils was investigated by using air- and spaceborne data acquired by Radar Aeroporte Multi-Spectral d'Etude des Signatures (RAMSES), Systeme Experimental de Teledetection Hyperfrequence Imageur (SETHI), and RADARSAT-2 sensors over several study sites in France. Fully polarimetric data at ultrahigh frequency, X-, C-, L-, and P-bands were compared. The results show that the main polarimetric parameters studied (entropy, α angle, and anisotropy) are not very sensitive to the variation of the soil surface parameters. Low correlations are observed between the polarimetric and soil parameters (moisture content and surface roughness). Thus, the polarimetric parameters are not very relevant to the characterization of the soil surface over bare agricultural areas.

A Multifrequency Polarimetric SAR Processing Chain to Observe Oil Fields in the Gulf of Mexico

Within the National Environmental Satellite, Data, and Information Service, National Oceanic and Atmospheric Administration, multiplatform synthetic aperture radar (SAR) imagery is being used to aid post hurricane and postaccident response efforts in the Gulf of Mexico, such as in the case of the recent Deepwater Horizon oil spill. The main areas of interest related to such disasters are the following: (1) to identify oil pipeline leaks and other oil spills at sea and (2) to detect man-made metallic targets over the sea. Within the context of disaster monitoring and response, an innovative processing chain is proposed to observe oil fields (i.e., oil spills and man-made metallic targets) using both Land C-band full-resolution and fully polarimetric SAR data. The processing chain consists of two steps. The first one, based on the standard deviation of the phase difference between the copolarized channels, allows oil monitoring. The second one, based on the different symmetry properties that characterize man made metallic targets and natural distributed ones, allows man made metallic target observation. Experiments, accomplished over single-look complex L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and C-band RADARSAT-2 fully polarimetric SAR data gathered in the Gulf of Mexico and related to the Deepwater Horizon accident, show the effectiveness of the proposed approach. Furthermore, the proposed approach, being able to process both Land C-band fully polarimetric and full resolution SAR measurements, can take full benefit of both the ALOS PALSAR and RADARSAT-2 missions, and therefore, it allows enhancing the revisit time and coverage which are very critical issues in oil field observation.

Detection of oyster habitat in tidal flats using multi-frequency polarimetric SAR data

Exposed oyster reefs in tidal flats have complex and rough surfaces because of their unique surface texture, which are quite distinct from the surrounding mud or sand flats. Here we investigate the microwave signatures, backscattered from naturally distributed oyster reefs in tidal flats, utilizing the polarimetric analysis techniques to fully polarimetric RADARSAT-2 (C-band) and ALOS PALSAR (L-band) data. The study areas include the tidal flats around Jebu Island and Hampyung Bay on the west coast of the Korean peninsula. We analyzed the microwave scattering mechanisms associated with oyster reefs and surrounding areas using the polarimetric target decomposition theorem and quantitatively measu ρ HHVV red target depolarization effects (the cross-polarized ratio (HV/VV), the co-polarized correlation coefficient ( ρ HHVV), and the co-polarized phase difference between HH and VV). On the basis of a large increase in the cross-polarized backscattering (HV) in the C-band SAR data, one can observe strong volume (or multiple) scattering and depolarization effects over oyster reefs areas, whereas only surface scattering was dominant in most parts of the background tidal areas. In oyster reefs, the proportion of volume scattering and the cross-polarized ratio were greater than 0.7 and −8 dB, respectively. These scattering characteristics were also verified from in-situ measurements in the field using a ground-based polarimetric scatterometer system. However, almost no difference was observed between the scattering signatures of oyster reefs and background mudflat areas from L-band data, which have a considerably longer wavelength than C-band. The study clearly suggests that multi-frequency (C- and L-band) polarimetric SAR systems can be used to detect the naturally distributed oyster reefs in tidal flats.

Information compression and speckle reduction for multifrequency polarimetric SAR images based on kernel PCA

Multifrequency polarimetric SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in several images, and redundancies exist between different bands and polarizations. Similar to signal-polarimetric SAR image, multifrequency polarimetric SAR image is corrupted with speckle noise at the same time. A method of information compression and speckle reduction for multifrequency polarimetric SAR imagery is presented based on kernel principal component analysis (KPCA). KPCA is a nonlinear generalization of the linear principal component analysis using the kernel trick. The NASA/JPL polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. The experimental results show that KPCA has better capability in information compression and speckle reduction as compared with linear PCA.

Estimation of Forest Fuel Load From Radar Remote Sensing

Understanding fire behavior characteristics and planning for fire management require maps showing the distribution of wildfire fuel loads at medium to fine spatial resolution across large landscapes. Radar sensors from airborne or spaceborne platforms have the potential of providing quantitative information about the forest structure and biomass components that can be readily translated to meaningful fuel load estimates for fire management. In this paper, we used multifrequency polarimetric synthetic aperture radar (SAR) imagery acquired over a large area of the Yellowstone National Park by the Airborne SAR sensor to estimate the distribution of forest biomass and canopy fuel loads. Semiempirical algorithms were developed to estimate crown and stem biomass and three major fuel load parameters, namely: 1) canopy fuel weight; 2) canopy bulk density; and 3) foliage moisture content. These estimates, when compared directly to measurements made at plot and stand levels, provided more than 70% accuracy and, when partitioned into fuel load classes, provided more than 85% accuracy. Specifically, the radar-generated fuel parameters were in good agreement with the field-based fuel measurements, resulting in coefficients of determination of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =85 for the canopy fuel weight, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 </sup> =0.84 for canopy bulk density, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.78 for the foliage biomass

The Bayesian Hierarchical Classifier (BHC) and Its Application to Short Vegetation Using Multifrequency Polarimetric SAR

Given an image of a scene comprised of a number of distinct terrain classes, the optimum Bayesian classifier (OBC) provides the highest possible classification accuracy of the imaged scene, provided we have a priori knowledge of the probability density function (pdf) of the sensor's output for each terrain class. If the imaging sensor consists of multiple channels, application of OBC requires knowledge of the joint pdf of the observations made by all the channels. In practice, the volume of data needed in order to generate an accurate multidimensional pdf far exceeds the size of available datasets. The data-size requirement may be relaxed by assuming the pdfs to be Gaussian in form, but such an assumption leads to suboptimum classification performance. This paper addresses the data size issue by (1) taking advantage of the maximum-entropy density estimation (MEDE) technique introduced in a companion paper and (2) using marginal pdfs in a hierarchical approach. Using multidate synthetic aperture radar observations, it was shown that the Bayesian hierarchical classifier introduced in this paper can classify short vegetation classes with an accuracy of 93%, without retraining, compared with an accuracy of 84% for the maximum-likelihood estimator (with Gaussian assumption) and only 74% with ISODATA.

Optimum model-based segmentation techniques for multifrequency polarimetric SAR images of urban areas

A new technique, named diagonal polarimetric merge-using-moments (DPOL MUM), is proposed for the segmentation of multifrequency polarimetric synthetic aperture radar (SAR) images that exploits the characteristic block diagonal structure of their covariance matrix. This technique is based on the newly introduced split-merge test, which has a reduced fluctuation error than the straight extension of the polarimetric test (POL MUM) and is shown to yield a more accurate segmentation on simulated SAR images. DPOL MUM is especially useful in the extraction of information from urban areas that are characterized by the presence of different spectral and polarimetric characteristics. Its effectiveness is demonstrated by applying it to segment a set of SIR-C images of the town of Pavia. The classification of the image segmented with DPOL MUM shows higher probability of correct classification compared to POL MUM and to a similar technique that does not use the correlation properties (MT MUM).

A new PCA-based method for data compression and enhancement of multi-frequency polarimetric SAR imagery

A new PCA-based method for an optimal representation of multi-frequency polarimetric SAR images is proposed. The method performs the simultaneous diagonalization of the signal and multiplicative noise covariance matrices via one orthogonal matrix. The covariance matrix of the multiplicative noise becomes an identity matrix, which implies that the variance of the noise in each new image is unity, and is uncorrelated between transformed images. The covariance matrix of the SAR images is transformed to a diagonal matrix whose diagonal elements are ordered in decreasing value, which means that the new images are uncorrelated and will be ordered by their variances (qualities). The theoretical analysis and the implementation procedure of the method are given. The method has been applied on real SAR images. The compression ability of the method is proved via a reconstitution process of the original SAR images from a small number of new images with a minimal loss of information.

Dual polarization stacked microstrip patch antenna array with very low cross-polarization

This paper describes the development and performance of a wideband dual linear polarization microstrip antenna array used in the Danish high-resolution airborne multifrequency polarimetric synthetic aperture radar, EMISAR. The antenna was designed for an operating frequency of 1.25 GHz/spl plusmn/50 MHz and was built as an array of 8/spl times/2 probe-fed stacked microstrip patches. The feeding network is constructed in microstrip and is capable of handling 6 kW of peak input-power at an altitude of 45000 ft (unpressurized). The impedance bandwidth (return loss better than -14 dB) of the antenna is 10%, the isolation between the horizontal and the vertical ports of the array is 50 dB and the cross-polarization suppression is 40 dB. A new design principle for simultaneously achieving very low cross-polarization and low side lobes in dual linear polarization antenna arrays has been applied.