Multidimensional Synthetic Aperture Radar Research Articles

Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated.

Multitemporal Polarimetric SAR Change Detection for Crop Monitoring and Crop Type Classification

The interpretation of multidimensional Synthetic Aperture Radar (SAR) data often requires expert knowledge. In fact, it requires to simultaneously consider several time series of polarimetric features to understand the physical changes of a target and its temporal evolution. To characterise the changes over time, Multitemporal Polarimetric SAR (MTPolSAR) change detection has been introduced in the literature in [1] and [2]. However, previous methods either only exploit intensity of changes or the resulting changed scattering mechanisms are not guaranteed to represent physical changes of the target. This paper presents a variation in the change detector used in [2] based on the difference of covariance matrices that characterise the polarimetric information, allowing for an intuitive representation and characterisation of physical changes of a target and its dynamics. We show the results of this method for monitoring growth stages of rice crops and present a novel application of the method for crop type mapping from MT-PolSAR data. We compare its performance with a neural network-based classifier that uses time series of PolSAR features derived from a target covariance matrix decomposition as input. Experimental results show that the classification performance of the proposed method and the baseline are comparable, with differences between the two methods in the overall balanced accuracy and the F1-macro metrics of around 2% and 3%, respectively. The method presented here achieves similar classification performances of a traditional PolSAR data classifier while providing additional advantages in terms of interpretability and insights about the physical changes of a target over time.

Shannon Entropy for the $\mathcal {G}^0_I$ Model: A New Segmentation Approach

Synthetic aperture radar (SAR) has been successfully used as a remote sensing tool. However, SAR images are contaminated by speckle noise and require specialized postprocessing procedures; e.g, tailored segmenters. The G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> distribution is a flexible model for SAR intensities because of its ability at describing heterogeneous clutters. Furthermore, applying information theory measures (e.g., entropy) to extract features in SAR imagery processing has achieved a prominent position. In this article, we derive both a closed-form expression for the G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> Shannon entropy and some of its mathematical properties. Consequently new entropy-based segmentation procedures for multidimensional SAR intensities-assuming independence or some dependence pattern-are also proposed. Finally, applications to real SAR imagery point out the proposed entropy-based segmenters can be more efficient than other well-defined methods, like the clustering by gamma mixture models.

A Sparse Manifold Classification Method Based on a Multi-Dimensional Descriptive Primitive of Polarimetric SAR Image Time Series

Classification using the rich information provided by time-series and polarimetric Synthetic Aperture Radar (SAR) images has attracted much attention. The key point is to effectively reveal the correlation between different dimensions of information and form a joint feature. In this paper, a multi-dimensional SAR descriptive primitive for each single pixel is firstly constructed, which in the polarimetric scale obtains incoherent information through target decompositions while in the time scale obtains coherent information through stochastic walk. Secondly, for the purpose of feature extraction and dimension reduction, a special feature space mapping for the descriptive primitive of the whole image is proposed based on sparse manifold expression and compressed sensing. Finally, the above feature is inputted into a support vector machine (SVM) classifier. This proposed method can inherently integrate the features of polarimetric SAR times series. Experiment results on three real time-series polarimetric SAR data sets show the effectiveness of our presented approach. The idea of a multi-dimensional descriptive primitive as a convenient tool also opens a new spectrum of potential for further processing of polarimetric SAR image time series.

Detection of Partially Coherent Scatterers in Multidimensional SAR Tomography: A Theoretical Study

Multidimensional synthetic aperture radar (SAR) imaging is a technique based on coherent SAR data combination for space (full 3-D) and space/deformation-velocity (4-D) analysis. It extends SAR interferometry and differential interferometry concepts, offering new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting scatterers showing partial correlation properties induced by simultaneous acquisitions from satellite formations or an uneven temporal distribution of satellite constellations. To this end, we first provide a general model for the pixel imaged by a multidimensional SAR system. Then, we design a constant false alarm rate (CFAR) decision rule accounting for the presence of partially coherent scatterers. At the analysis stage, we assess the performance of the new detector also in comparison with a previously proposed CFAR scheme, developed in the context of SAR tomography for fully coherent scatterers.

An Efficient Tomographic Inversion Approach for Urban Mapping Using Meter Resolution SAR Image Stacks

This letter describes an efficient approach of multidimensional synthetic aperture radar (SAR) imaging for urban mapping. The proposed approach is an integration of tomographic SAR inversion and the well-known persistent scatterer interferometry (PSI). It consists of three steps: first, a global estimation of the topography and motion parameters using efficient algorithms such as PSI; second, a single and double scatterer discrimination step based on the results of the first step; finally, a tomographic SAR inversion, which is performed on the preclassified double scatterers, using the prior knowledge obtained in the first step, retrieving the topography and motion parameters of both scatterers. The proposed approach has been tested on a dozen of TerraSAR-X high-resolution spotlight image stacks. In this letter, examples from Las Vegas and Berlin are presented. The results are comparable with the one obtained by the most computationally expensive tomographic SAR algorithms (e.g., SLIMMER) only and saves computational time by a factor of 50.

Analysis of multi-dimensional SAR for determining the thickness of thin sea ice in the Bohai Sea

Flat thin ice (<30 cm thick) is a common ice type in the Bohai Sea, China. Ice thickness detection is important to offshore exploration and marine transport in winter. Synthetic aperture radar (SAR) can be used to acquire sea ice data in all weather conditions, and it is a useful tool for monitoring sea ice conditions. In this paper, we combine a multi-layered sea ice electromagnetic (EM) scattering model with a sea ice thermodynamic model to assess the determination of the thickness of flat thin ice in the Bohai Sea using SAR at different frequencies, polarization, and incidence angles. Our modeling studies suggest that co-polarization backscattering coefficients and the co-polarized ratio can be used to retrieve the thickness of flat thin ice from C- and X-band SAR, while the co-polarized correlation coefficient can be used to retrieve flat thin ice thickness from L-, C-, and X-band SAR. Importantly, small or moderate incidence angles should be chosen to avoid the effect of speckle noise.

Concept on Multidimensional Space Joint-observation SAR

The objective of this paper is to investigate the connotation, necessity and tendency of Synthetic Aperture Radar (SAR) imaging technology within the framework of multidimensional space joint-observation, which are polarimetry space, frequency space, angle space and time space, on the basis of the key evolvement phases of SAR imaging technology. Furthermore，the definition of Multidimensional Space Joint-observation SAR (MSJosSAR) is given based on the demand of information fusion of multidimensional space joint observation SAR images. After that, the advantage of MSJosSAR is revealed by using Kronecker product decomposition technology for better understanding of target scattering mechanisms. Besides, the hypothesis and basic framework on which the MSJosSAR signal processing rely is listed and illustrated. Finally, the number of joint observation spaces of typical SAR configurations is enumerated.

Potential of SAR for monitoring transportation infrastructures: an analysis with the multi-dimensional imaging technique

Differential interferometric synthetic aperture radar (SAR) has proven to be effective for accurate localization and monitoring of the displacement of ground targets. The high accuracy and spatial density of the measurements make this technique cost effective compared to the classical geodetic techniques typically used in the risk monitoring context. Ground infrastructure monitoring is typically carried out with in situ sensors. The new generation of high-resolution SAR sensors, however, allows one to acquire data sets with a spatial resolution reaching metric/submetric values. Here we investigate the application of a multi-dimensional SAR imaging technique, which is an extension of classical differential interferometric techniques, to very high resolution TerraSAR-X data in order to demonstrate the potential of this technology for monitoring of transportation infrastructures.

Unsupervised classification of polarimetric SAR image with dynamic clustering: An image processing approach

This paper proposes a new unsupervised classification approach for automatic analysis of polarimetric synthetic aperture radar (SAR) image. Classification of the information in multi-dimensional polarimetric SAR data space by dynamic clustering is addressed as an optimization problem and two recently proposed techniques based on particle swarm optimization (PSO) are applied to find optimal (number of) clusters in a given input data space, distance metric and a proper validity index function. The first technique, so-called multi-dimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem, fractional global best formation (FGBF) technique is then presented, which basically collects all promising dimensional components and fractionally creates an artificial global-best particle (aGB) that has the potential to be a better ''guide'' than the PSO's native gbest particle. In this study, the proposed dynamic clustering process based on MD-PSO and FGBF techniques is applied to automatically classify the color-coded representations of the polarimetric SAR information (i.e. the type of scattering, backscattering power) extracted by means of the Pauli or the Cloude-Pottier decomposition algorithms. The performance of the proposed method is evaluated based on fully polarimetric SAR data of the San Francisco Bay acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band. The proposed unsupervised technique determines the number of classes within polarimetric SAR image for optimal classification performance while preserving spatial resolution and textural information in the classified results. Additionally, it is possible to further apply the proposed dynamic clustering technique to higher dimensional (N-D) feature spaces of fully polarimetric SAR data.

Detection of Single Scatterers in Multidimensional SAR Imaging

Multidimensional synthetic aperture radar (SAR) imaging is a technique based on coherent SAR data combination for space (full 3-D) and space deformation-velocity (4-D) analysis. It is an extension of the concepts of SAR interferometry and differential interferometry SAR and offers new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting single scatterers for localization and monitoring issues. To this end, we resort to a constant false alarm rate (CFAR) detection scheme which can be synthesized according to three different design criteria: generalized likelihood ratio test, Rao test, and Wald test. At the analysis stage, the performance of the aforementioned detector is compared to that of a previously proposed CFAR scheme, based on the multi-interferogram complex coherence and widely used in persistent scatterer interferometry. The analysis is conducted both on simulated and on real SAR data, acquired by ERS-1/2 satellites. Finally, Cramer-Rao lower bounds for the estimation of the scatterer elevation and velocity are provided.

On the Extension of Multidimensional Speckle Noise Model From Single-Look to Multilook SAR Imagery

Speckle noise represents one of the major problems when synthetic aperture radar (SAR) data are considered. Despite the fact that speckle is caused by the scattering process itself, it must be considered as a noise source due to the complexity of the scattering process. The presence of speckle makes data interpretation difficult, but it also affects the quantitative retrieval of physical parameters. In the case of one-dimensional SAR systems, speckle is completely determined by a multiplicative noise component. Nevertheless, for multidimensional SAR systems, speckle results from the combination of multiplicative and additive noise components. This model has been first developed for single-look data. The objective of this paper is to extend the single-look data model to define a multilook multidimensional speckle noise model. The asymptotic analysis of this extension, for a large number of averaged samples, is also considered to assess the model properties. Details and validation of the multilook multidimensional speckle noise model are provided both theoretically and by means of experimental SAR data acquired by the experimental synthetic aperture radar system, operated by the German Aerospace Center

On the design of SAR apertures using the Cramer-Rao bound

The Cramer-Rao inequality is applied to the likelihood function of the synthetic aperture radar (SAR) scatterer parameter vector to relate the choice of flight path to estimation performance. Estimation error bounds for the scatterer parameter vector (including height) are developed for multi-dimensional synthetic apertures, and quantify the performance enhancement over a limited sector of the image plane relative to standard-aperture single-pass SAR missions. An efficient means for the design and analysis of SAR waveforms and flight paths is proposed using simulated scattering models that are limited in size. Comparison of the error bounds to those for standard-aperture SAR show that estimates of scatterer range and cross-range positions are accurate for multi-dimensional aperture SAR, even with the additional estimator for height. Furthermore, multi-dimensional SAR is shown to address the layover problem

Polarimetric speckle noise effects in quantitative physical parameters retrieval

Quantitative estimation of physical parameters of the Earth&apos;s surface is currently an active area of research. Multidimensional synthetic aperture radar (SAR) systems appear in this framework as a tool capable of performing this task on a global scale and independently of the weather conditions. In particular, polarimetric SAR data have been successfully employed, in combination with different electromagnetic inversion algorithms, to extract physical information from measured data. Nevertheless, SAR data are affected by speckle noise. The influence of speckle noise and its filtering in the quantitative estimation of physical parameters is analysed. To perform this study, a novel model for speckle noise in multidimensional SAR data is considered. Finally, the effect on the quantitative estimation of surface parameters is investigated.

Polarimetric sar speckle noise model

Synthetic aperture radar (SAR) data are affected by speckle noise, originated by the SAR system's coherent nature. The problem of speckle noise in one-dimensional (1-D) data is already solved, as speckle has a multiplicative characteristic. SAR polarimetry represents an extension to multidimensional data by the use of polarization wave diversity. As a consequence of the existence of a correlation degree between the SAR images, the 1-D speckle noise model cannot be extended to multidimensional SAR data. This paper is devoted to present a completely new speckle noise model for the complex covariance matrix describing polarimetric SAR data in the distributed scatterers case. As is shown, this new model is able to identify which are the noise mechanisms in all the covariance matrix elements. The speckle noise model is validated by using real L-band polarimetric data acquired with the German E-SAR sensor.