Polarimetric Synthetic Aperture Radar Data Research Articles

A joint change detection method on complex-valued polarimetric synthetic aperture radar images based on feature fusion and similarity learning

ABSTRACT 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 from optical image processing to synthetic aperture radar (SAR) image processing. However, the polarization information, which is critical in POLSAR data, has been discarded under normal situations. This paper introduces a novel joint change detection network based on similarity learning for coregistered complex-valued SAR data. A pseudo-siamese network takes both amplitude information and polarization information of POLSAR data as the input. The fusion of low-level, middle-level and high-level features enables the network to keep high-resolution and have strong representation ability during training procedures. Our novel sub-networks, which we term C2-Net and Intensity-Net, deal with the covariance matrix of complex SAR data and amplitude SAR data, respectively. The Intensity-Net works as a typical classification network and detects targets directly. The C2-Net attempts to find the relationship between two SAR data patches. An improved cosine similarity function is used to measure the similarity between two generated feature vectors in C2-Net. Output probability vectors of the two sub-networks are combined for final change detection. The two sub-networks are trained jointly and simultaneously. Control experiments show that proposed improvements are working. Experimental results on dual-polarization complex-valued SAR data from Sentinel-1 demonstrate the effectiveness of the proposed method.

Eigen vector-based classification of pearl millet crop in presence of other similar structured (sorghum and maize) crops using fully polarimetric Radarsat-2 SAR data

The effect of structure and phenology of dryland crops on SAR scattering mechanisms is under explored and under reported. This study aims to understand the complex scattering mechanisms occurring in similar structured crops and to discriminate pearl-millet crop using single date Radarsat-2 SAR data. The study arrived at few significant findings that the panicle stage isthe critical stage for discriminating pearl-millet from similar structured sorghum and maize crops. Alpha angle extracted from eigen-vector based decomposition was identified as the discriminating factor. Average alpha value almost reached 55° due to dihedral scattering occurring in the prominent panicles which emerge early in pearl millet compared to maize and sorghum. Wishart H-A-α technique discriminated pearl millet, maize and sorghum crops with a producer's accuracy of about 87.8%, 71.6% and 71.4%, respectively. Paddy was classified with highest accuracy of 95.8% due to occurrence of dominant double bounce scattering in most of the fields.

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: 50 km2); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: 113 km2); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: 5 km2). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field

Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task nowadays. One significant barrier lies in the speckle effect embedded in the PolSAR imaging process, which greatly degrades the quality of the images and further complicates the classification. To this end, we present a novel PolSAR image classification method that removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via the Markov random field (MRF). Especially, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises. Then, a classification map is obtained by applying a convolutional neural network with data augmentation on the extracted features, where local consistency is implicitly involved, and the insufficient label issue is alleviated. Finally, we refine the classification map by MRF to enforce contextual smoothness. We conduct experiments on two benchmark PolSAR data sets. Experimental results indicate that the proposed method achieves promising classification performance and preferable spatial consistency.

Change Detection in Multilook Polarimetric SAR Imagery With Determinant Ratio Test Statistic

In this article, we propose a determinant ratio test (DRT) statistic to measure the similarity of two covariance matrices for unsupervised change detection in polarimetric radar images. The multilook complex covariance matrix is assumed to follow a scaled complex Wishart distribution. In doing so, we provide the distribution of the DRT statistic that is exactly Wilks’s lambda of the second kind distribution, with density expressed in terms of Meijer G-functions. Due to this distribution, the constant false alarm rate (CFAR) algorithm is derived in order to achieve the required performance. More specifically, a threshold is provided by the CFAR to apply to the DRT statistic producing a binary change map. Finally, simulated and real multilook polarimetric SAR (PolSAR) data are employed to assess the performance of the method and is compared with the Hotelling–Lawley trace (HLT) statistic and the likelihood ratio test (LRT) statistic.

A Computationally Efficient Hybrid Framework for Polarimetric Calibration of Quad‐Pol SAR Data

AbstractPolarimetric Synthetic Aperture Radar (PolSAR) calibration is an essential preprocessing step that must be performed to ensure that the data quality is adequate. This, in turn, helps to minimize the propagation of errors in any further data processing or information extraction. Crosstalk and channel imbalance are two major distortions found to be present in the uncalibrated polarimetric SAR data. The PolSAR calibration mainly aims to reduce these two distortions revealing the true scattering pattern of the targets. In this regard, Quegan's algorithm and Ainsworth algorithm are two widely used algorithms for the PolSAR calibration. In this study, the accuracy and efficiency of these two algorithms have been thoroughly compared using suitable metrics. It has been shown that the Ainsworth algorithm performs better than Quegan's in terms of accuracy at the cost of poor computational efficiency. The data quality metrics also highlight the better calibration accuracy of the Ainsworth algorithm. The issue of higher computational complexity has been effectively addressed by coupling both of these algorithms. Evidently, the computational cost has been reduced in the case of the proposed algorithm. The polarization orientation angle (POA) shift is another distortion caused by the topographic variations present in the target scene. Therefore, correction of POA shift has been incorporated in this research by coupling it with the PolSAR calibration. Subsequently, the improvement in the scattering has been observed. In essence, the proposed algorithm coupled with the correction of POA shift rectifies the major polarimetric distortions with adequate accuracy and computational efficiency.

Polarimetric calibration of spaceborne and airborne multifrequency SAR data for scattering-based characterization of manmade and natural features

The Polarimetric Synthetic Aperture Radar (PolSAR) systems use electromagnetic radiations of different polarizations in the microwave frequency to collect the scattering information from targets on the Earth. Nevertheless, as with any other electronic device, the PolSAR systems are also not ideal and subjected to distortions. The most important of these distortions are the polarimetric distortions caused due to the channel imbalance, phase bias, and crosstalk between the different polarization channels. For the spaceborne PolSAR systems, the Earth’s ionosphere also contributes to an additional polarimetric distortion known as the Faraday rotation. An effort was made in this study to perform the polarimetric calibration of the Quad-pol and Compact-pol PolSAR datasets acquired using different airborne and spaceborne PolSAR systems to estimate and minimize these polarimetric distortions. The investigation was also done to analyze the impact of these polarimetric distortions on the scattering mechanisms from ground targets and on its dependency on the radar wavelength. The study was done using the UAVSAR L-band Quad-pol dataset, RADARSAT-2 Quad-pol dataset, ALOS-2 PALSAR-2, ISRO's L&S- Band Airborne SAR (LS-ASAR) Quad-pol and Compact-pol datasets, and the RISAT-1 Compact-pol dataset. Calibration of the airborne PolSAR data was carried to understand the level of polarimetric distortions in the LS-ASAR product that is a precursor mission to the spaceborne Dual-Frequency L&S Band NASA-ISRO Synthetic Aperture Radar (NISAR) mission. It is understood that the crosstalk is the dominant polarimetric distortion, which severely affects the PolSAR datasets compared to the other polarimetric distortions, and it is more for the higher wavelength PolSAR systems. The Quegan, Improved Quegan, and Ainsworth algorithms for crosstalk estimation and minimization was performed for the different Quad-pol datasets and it was found that the Improved Quegan algorithm is suitable for removing crosstalk from datasets having high crosstalk and the Ainsworth algorithm is suitable for removing crosstalk from datasets having low crosstalk. The Freeman method of the polarimetric calibration was implemented for the compact-pol datasets and it was able to considerably minimize the polarimetric distortions. The coherency matrix, scattering matrix, model-based decomposition, polarimetric signatures, and roll invariant parameter-based analysis revealed that all the datasets after polarimetric calibration were showing the correct scattering responses expected from the ground targets.

Development of classification criteria for dominant scattering from the lunar surface: An application of MiniSAR data

The dominant scattering volume, surface, and double-bounce, as retrieved from the hybrid or compact polarimetric Synthetic Aperture Radar (SAR) data of the lunar surface, are found to be intermingled. These dominant scattering can also lead to ambiguous interpretation of volume, surface, and double-bounce scattering due to uncertainty in the transmission of a perfectly circular Electromagnetic (EM) wave. Hence, classification criteria are proposed to identify volume, surface, and double-bounce dominant scattering. For this purpose, we have utilized the conformity coefficient (μ) parameter-based method for identifying these dominant scattering. Firstly, dominant pixels have been extracted using the dominant scattering equations. These equations are presented in this work based on experimental analysis. Further, proposed criteria have been developed using the mapping between the extracted pixels from the dominant scattering equations and conformity coefficient (μ). The performance of the proposed classification criteria is examined over 33 subsamples from MiniSAR data of Peary, Rozhdestvenskiy, and Hermite craters. Further, the comparison of the proposed criteria with other decomposition methods have been performed. The experimental results from these comparisons indicate that the proposed method can further strengthen the existing decomposition-based analysis. The applicability of the proposed criteria was tested using ANOVA and Tukey’s HSD test, which supports the proposed approach.

Semisupervised Classification With Adaptive Anchor Graph for PolSAR Images

With the rapid development of the remote sensing area, now, we can collect massive data. Labeling polarimetric synthetic aperture radar (PolSAR) data is a labor-intensive and time-consuming work. The performance of the supervised model heavily depends on the number of training samples and there is a large number of unlabeled data unutilized, which contains statistical information of the PolSAR image. To solve this issue, in this letter, an adaptive anchor graph regularization (AdaAGR) is proposed for PolSAR images. The accuracy of the conventional anchor graph-based method is dependent on the choice of the number of nearest anchors. Applying a global number of nearest anchors to the anchor graph method ignores the local data density. Low data density corresponds to a small number of nearest anchors and vice versa. A global number of nearest anchors may limit the performance of classification. In this letter, an adaptive anchor graph strategy is proposed. First, cluster centers generating from Wishart clustering are served as graph anchors. Second, the distances between the data point and its closest anchors are calculated in ascending order. Then, an optimal number of nearest anchors is selected for every pixel. Third, the similarity between data and its neighboring anchors is measured, and then, an anchor graph is constructed. Finally, the anchor graph is regularized and the unlabeled samples are predicted. Experimental results on two real PolSAR data sets show that the classification accuracy can be further improved by employing the proposed adaptive anchor graph strategy.

SLIC Superpixel Segmentation for Polarimetric SAR Images

Superpixel segmentation approaches for polarimetric synthetic aperture radar (SAR) images have only been studied in recent years. Simple linear iterative clustering (SLIC) is a simple and efficient superpixel segmentation method, first proposed for optical images. It basically includes three implementation steps, i.e., initialization, local <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering, and postprocessing. The challenge of applying SLIC to polarimetric SAR images lies in constructing the effective spatial and feature similarity and proposing the efficient segmentation procedure. In this study, to address both issues, we modify the SLIC clustering function to adapt the characteristics of polarimetric statistical measures. A new initialization method is proposed, which exploits the image gradient information to produce robust cluster centers. Furthermore, in an effort to give a comprehensive comparison and provide a fair assessment of the feature similarities for polarimetric SAR imagery, four classic statistical distances, among which two were not studied along with the SLIC previously, are embedded in the modified clustering function. The proposed method is validated by comparing with state-of-the-art SLIC-based algorithms and also the Ncut and TurboPixel algorithms. Experiments on extensive polarimetric SAR data sets show that the proposed method can significantly improve the segmentation results, with producing better boundary adherence and compact as well as uniform superpixels. We also obtain distinct conclusions that are different from the existing studies when investigating the performances of the statistical measures.

Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning

This study presents a demonstration of the applicability of machine learning techniques for the retrieval of crop height in corn fields using space-borne PolSAR (Polarimetric Synthetic Aperture Radar) data. Multi-year RADARSAT-2 C-band data acquired over agricultural areas in Canada, covering the whole corn growing period, are exploited. Two popular machine learning regression methods, i.e., Random Forest Regression (RFR) and Support Vector Regression (SVR) are adopted and evaluated. A set of 27 representative polarimetric parameters are extracted from the PolSAR data and used as input features in the regression models for height estimation. Furthermore, based on the unique capability of the RFR method to determine variable importance contributing to the regression, a smaller number of polarimetric features (6 out of 27 in our study) are selected in the final regression models. Results of our study demonstrate that PolSAR observables can produce corn height estimates with root mean square error (RMSE) around 40–50 cm throughout the growth cycle. The RFR approach shows better overall accuracy in corn height estimation than the SVR method in all tests. The six selected polarimetric features by variable importance ranking can generate better results. This study provides a new perspective on the use of PolSAR data in retrieving agricultural crop height from space.

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).

A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

Man-Made Target Detection of PolSAR Image Based on Local Convolution Sparse Representation

Man-made target detection of PolSAR image is an important part of the application of remote sensing data, and generally, the target occupies very few pixels of the image. Convolutional sparse representation (CSR) is proved able to effectively combine polarimetric and spatial information of a PolSAR image to achieve target detection. However, the acquisition of prior knowledge and the modeling of background are difficult. Moreover, the solution process is highly dependent on the alternating direction method of multipliers (ADMMs) algorithm, which introduces new parameters and has high computational complexity. To solve these problems, this letter proposes a man-made target detection method for PolSAR images based on local CSR (Local-CSR). In this method, a novel target detection framework to combine the polarimetric and spatial information based on CSR is proposed, the target dictionary is constructed by the Local-CSR to avoid the aforementioned problems of ADMM solution procedure and finally realize the man-made target detection of the PolSAR image. Two sets of fully polarimetric SAR data sets are used to demonstrate the performance and the experimental results prove the capacity and validity of the proposed method.

A Multichannel Fusion Convolutional Neural Network Based on Scattering Mechanism for PolSAR Image Classification

Polarimetric features extracted from the polarimetric synthetic aperture radar data contain a wealth of target scattering information, but usually lead to the problems, such as network learning burden and high computational consumption. A multichannel fusion convolutional neural network based on scattering mechanisms was presented in this letter. First, the polarimetric features were divided into three categories according to their corresponding scattering mechanisms, and put into three network channels, respectively. Second, a new feature output was constructed based on the fusion of three-channel output features. Third, the four output features were cascaded through two fully connected layers and the Softmax classifier to get the classification result. Moreover, a new loss function was defined, combining cross entropy and average cross entropy to prevent network overfitting. Experimental results on airborne synthetic aperture radar (AIRSAR) and GF-3 data set verified the effectiveness of the proposed method in the aspect of classification accuracy and small sample.

PolSAR Image Classification Using Hybrid Conditional Random Fields Model Based on Complex-Valued 3-D CNN

In this article, we propose a hybrid conditional random field model based on complex-valued 3-D convolutional neural network (C3NN), named as HCRF-C3NN, for polarimetric synthetic aperture radar (PolSAR) image classification. HCRF-C3NN combines the modeling power of random fields with the representation-learning ability of deep learning model for PolSAR image. By C3NN, HCRF-C3NN exploits deep amplitude and phase information of PolSAR data and, thus, measures the class probabilities in the framework of random fields. Additionally, HCRF-C3NN captures the spatial label interactions effectively in PolSAR image classification by the following work: 1) Based on the C3NN-based class probabilities, the relative entropy of class distributions is derived to describe the local structure and, thus, regulates the pairwise label interactions, which enhances the accuracy of edge location in the classification; 2) a product-of-expert potential is introduced to enforce label consistency, and, thus, HCRF-C3NN is more robust against speckle of PolSAR image and achieves smoother classification within the consistent class region. At last, to capture PolSAR image information in a more complete manner, the deep features and PolSAR scattering statistics are integrated into HCRF-C3NN based on Bayesian fusion. In this way, HCRF-C3NN effectively explores spatial correlation, scattering statistics, and deep features of PolSAR data in the framework of random fields. The experimental results demonstrate the superiority of HCRF-C3NN over the recent deep learning models for PolSAR image classification.

Semisupervised Classification of PolSAR Images Using a Novel Memory Convolutional Neural Network

To improve the classification performance of the convolutional neural network (CNN) for polarimetric synthetic aperture radar (PolSAR) images with limited labeled samples, this letter proposes a memory CNN (MCNN) for semisupervised PolSAR image classification using both the labeled and unlabeled samples. Specifically, the MCNN introduces a memory module to realize an assimilation–accommodation interaction between the network and the module in the model training process. Compared with the traditional CNN-based methods, the advantage of the introduced interaction mechanism can exploit the memory information during the model training including both the learned feature representation and the model inference uncertainty. Under the framework of memory mechanism, the semisupervised learning can be implemented simply and effectively by introducing an unsupervised memory loss. We evaluate the proposed method on three benchmark PolSAR data sets. The experimental results show the advantages of the MCNN over the supervised, semisupervised, and unsupervised methods in the PolSAR image classification with limited labeled samples.

Multi-Temporal Speckle Filtering of Polarimetric P-Band SAR Data over Dense Tropical Forests: Study Case in French Guiana for the BIOMASS Mission

The purpose of this paper is twofold, considering first the generalization of a multichannel speckle filter in order to handle temporal stacks of polarimetric SLC SAR data, and secondly the development of an ad hoc performance indicator based on the Polarimetric Orientation Angle (POA) in order to better estimate the resulting speckle reduction than the standard Equivalent Number of Looks (ENL) over densely vegetated regions, like tropical forests. Being based on the ability of PolSAR measurements to retrieve ground slopes through dense vegetation, this performance indicator requires the use of low frequencies such as P-band, as well as fully polarimetric data. This study has thereby a particular interest in the context of the upcoming BIOMASS spaceborne mission whose launch is scheduled in 2023, and makes use of data from the TropiSAR airborne campaign initiated in the early stage of the mission developments. Conducted over several test sites of tropical dense forests in French Guiana, this campaign gives us the opportunity herein to exploit P-band temporal stacks with repeated time intervals transposable to BIOMASS in terms of signal decorrelation. The application of the generalized multichannel speckle filter to the Paracou test site dataset reveals the limitations of the standard ENL analytical formula to assess speckle reduction in the case of spatially correlated media like dense forests, and for this purpose the interest of the correlation between POA and azimuthal slopes computed from an independent Digital Surface Model.

Semi-Supervised Classification for PolSAR Data With Multi-Scale Evolving Weighted Graph Convolutional Network

Although deep learning-based methods have been successfully applied to polarimetric synthetic aperture radar (PolSAR) image classification tasks, most of the available techniques are not suitable to deal with PolSAR data on irregular domains, e.g., superpixel graphs, because they are naturally designed as grid-based architectures in Euclidean space. To overcome this limitation and achieve robust PolSAR image classification, this article proposes the multiscale evolving weighted graph convolutional network, where weighted graphs based on superpixel technique and Wishart-derived distance are constructed to enable efficient handling of graphical PolSAR data representations. In this article, we derive a new architectural design named graph evolving module that combines pairwise latent feature similarity and kernel diffusion to refine the graph structure in each scale. Finally, we propose a graph integration module based on self-attention to perform robust hierarchical feature extraction and learn an optimal linear combination of various scales to exploit effective feature propagation on multiple graphs. We validate the superiority of proposed approach on classification performance with four real-measured datasets and demonstrate significant improvements compared to state-of-the-art methods. Additionally, the proposed method has shown strong generalization capacity across datasets with similar land covers.

Fully polarimetric synthetic aperture radar data classification using probabilistic and non-probabilistic kernel methods

ABSTRACT 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.