Classification Of Polarimetric SAR Data Research Articles

A multi-view ensemble model based on semi-supervised feature learning for small sample classification of PolSAR images

ABSTRACT Classification with a few samples of training set has been a long-standing issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. In fact, one of the most important challenges of the PolSAR image classification task is the number of labelled samples. In essence, training network by utilizing just few numbers of training samples cannot be led to the reliable result since the neural network is so sensitive to the number of training samples. In addition, in the real PolSAR image classification task, the huge number of training samples is not accessible. On the other hand, classification performance using just small number of labelled samples is not an accurate result. So, in this paper, aiming at the small number of training samples of the PolSAR image classification task, a novel ensemble self-supervised feature-learning (ESSFL) model is designed. The designed ESSFL can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, to utilize the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet-B0 is presented and utilized as the main section of the deep learning (DL) model to extract DL features of PolSAR data. Then, using an optimization function that constrains the cross-correlation matrix of various distortions of each sample to the identity matrix, the designed deep learning model can obtain the effective features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. Moreover, two ensemble learning (EL) models, feature-level and view-level ensemble, are proposed to increase the feature extraction capability and classification result by jointly using spatial features at different scales and polarimetric information at different bands. Finally, the stack of the obtained features and the main polarimetric information of PolSAR data can be input into classifier for the classification of PolSAR data. In this paper we use two different classifiers, the first one is the Random Forest (RF) classifier and the second one is the Support Vector Machine (SVM) classifier. The advantages of SVM include that it can be used to avoid the difficulties of using linear functions in the high-dimensional feature space, and the optimization problem is transformed into dual convex quadratic programs that are so usable for an actual image classification task. In addition, among all the available classification methods, random forests provide one of the highest accuracies. The random forest technique can also handle big data with numerous variables running into thousands. In fact, it can automatically balance data sets when a class is more infrequent than other classes in the data that is so important for a real PolSAR image classification task. Experimental results on three well-known PolSAR data sets illustrate that the designed ESSFL can extract more discriminant features using the designed deep learning model. In the end, the experiments prove that the designed ESSFL has a significant classification performance compared with different deep learning models in the case of small number of training samples, and also it can achieve a better result in the case of large number of training samples by comparison with the most of the deep learning models.

Local Window Attention Transformer for Polarimetric SAR Image Classification

Convolutional neural networks (CNNs) have recently found great attention in image classification since deep CNNs have exhibited excellent performance in computer vision. Owing to their immense success, of late, scientists are exploring the functionality of transformers in Earth observation applications. Nevertheless, the primary issue with transformers is that they demand significantly more training data than CNN classifiers. Thus, the use of these transformers in remote sensing is considered challenging, notably in utilizing polarimetric SAR (PolSAR) data, due to the insufficient number of existing labeled data. In this letter, we develop and propose a vision transformer-based framework that utilizes 3D and 2D CNNs as feature extractors and, in addition, local window attention for the effective classification of PolSAR data. Extensive experimental results demonstrated that the developed model PolSARFormer obtained better classification accuracy than the state-of-the-art vision Swin Transformer and FNet algorithms. The PolSARFormer outperformed the Swin Transformer and FNet by the margin of 5.86% and 17.63%, in terms of average accuracy in the San Francisco data benchmark. Moreover, the results over the Flevoland dataset illustrated that the PolSARFormer exceeds several other algorithms, including the ResNet (97.49%), Swin Transformer (96.54%), FNet (95.28%), 2D CNN (94.57%), and AlexNet (91.83%), with a kappa index of 99.30%. The code will be made available publicly at https://github.com/aj1365/PolSARFormer.

Crop Classification of Multitemporal PolSAR Based on 3-D Attention Module With ViT

Multi-temporal polarimertic SAR is considered to be very effective in crop classification and cultivated land detection, which has received much attention from researchers. Currently, for most multi-temporal polarimetric SAR data classification methods, the simultaneous temporal-polarimetric-spatial feature extraction capability has not been exploited sufficiently. Also, the diversity of different time and different polarimetric features has not been taken into account sufficiently. In this paper, we propose a classification model that combines a dual-stream network as a temporal-polarimetric-spatial feature extraction module with Vision Transformer(ViT) called Temporal-Polarimetric-Spatial Transformer(TSPT) to address the above problems. Secondly, a 3 dimension(3D) convolutional attention module that enables the network to weight the temporal dimension, polarimetric feature dimension and spatial dimension is developed, according to their importance. Experimental results on both UAVSAR and RADARSAT-2 datasets show that the proposed method outperforms ResNet.

The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data

The radiometric terrain correction (RTC) is an essential processing step for supervised classification applications of polarimetric synthetic aperture radar (PolSAR) over mountainous areas. However, the current angular variation effect (AVE) correction methods of three-step RTC processing are difficult to apply to PolSAR supervised classification because of the problem of interdependence between AVE correction and classification. To address this issue, based on the three-step semi-empirical RTC approach, we propose an improved AVE correction method suitable for the supervised classification of PolSAR. We make full use of the prior knowledge required for supervised classification and RTC processing, that is, samples and elevation data, to calculate the parameters of AVE correction by constructing a weight coefficient matrix. GaoFen-3 QPSI (C-band, quad-polarization) data were used to verify the proposed method. Experimental results showed that the proposed method is available and effective for PolSAR supervised classification. The new method can effectively remove the AVE effect in the PolSAR image, and the overall accuracy of PolSAR supervised classification can be improved about 9% compared to that without AVE correction. For the fine classification of forest types, the AVE correction can improve the classification accuracy by about 20%.

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data.

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.

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.

Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data

Recently, classification methods based on deep learning have attained sound results for the classification of Polarimetric synthetic aperture radar (PolSAR) data. However, they generally require a great deal of labeled data to train their models, which limits their potential real-world applications. This paper proposes a novel semi-supervised deep metric learning network (SSDMLN) for feature learning and classification of PolSAR data. Inspired by distance metric learning, we construct a network, which transforms the linear mapping of metric learning into the non-linear projection in the layer-by-layer learning. With the prior knowledge of the sample categories, the network also learns a distance metric under which all pairs of similarly labeled samples are closer and dissimilar samples have larger relative distances. Moreover, we introduce a new manifold regularization to reduce the distance between neighboring samples since they are more likely to be homogeneous. The categorizing is achieved by using a simple classifier. Several experiments on both synthetic and real-world PolSAR data from different sensors are conducted and they demonstrate the effectiveness of SSDMLN with limited labeled samples, and SSDMLN is superior to state-of-the-art methods.

Polarimetric Convolutional Network for PolSAR Image Classification

The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix only show a limited number of polarimetric information. In order to solve this problem, we propose a sparse scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on convolution network to combine this feature. Based on sparse scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for sparse scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding.

Adaptive Model-Based Classification of PolSAR Data

An adaptive classification is developed as a hybrid of the eigenvector-based and the model-based target decompositions for polarimetric synthetic aperture radar (PolSAR) data. The classification adopts the canonical scattering models that widely used in model-based decompositions to provide an improvement for the well-known ${H/\alpha }$ classification. First, a correspondence principle is adopted to adaptively identify the matched canonical models. The selected models are parallelly combined based on the scattering similarity for a fine depiction of the scattering mechanism then. Twelve classes are finally obtained, and each one carries a unique symbol to show a specific scattering. The classification does not depend on a particular data set, avoids the hard partitioning, and solves the obscures in ${H/\alpha }$ . Comparison on the real PolSAR data sets with ${H/\alpha }$ and the existing scattering similarity-based classification validates the better discrimination.

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.

Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived From a Geodesic Distance

In this letter, we propose a novel technique for obtaining scattering components from Polarimetric Synthetic Aperture Radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories i.e. odd-bounce, double-bounce and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of [J.-S. Lee, M. R. Grunes, E. Pottier, and L. Ferro-Famil, Unsupervised terrain classification preserving polarimetric scattering characteristics, IEEE Trans. Geos. Rem. Sens., vol. 42, no. 4, pp. 722731, April 2004.] based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 datasets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman-Durden scattering powers on an orientation angle (OA) corrected PolSAR image. Furthermore, (1) the scattering similarity is a completely non-negative quantity unlike the negative powers that might occur in double- bounce and odd-bounce scattering component under Freeman Durden decomposition (FDD), and (2) the methodology can be extended to more canonical targets as well as for bistatic scattering.

PolSAR image classification based on Laplacian Eigenmaps and superpixels

This paper proposes a method of polarimetric synthetic aperture radar (PolSAR) image classification using improved superpixel segmentation and manifold learning. Firstly, a 27-dimension polarimetric feature space is extracted by simple arithmetic operations of polarimetric parameters and polarimetric target decomposition. Secondly, Laplacian Eigenmap (LE) algorithm is used to reduce the dimension of the 27-dimension polarimetric features. This algorithm can reduce redundant information in feature space and extract the main information. Then, the paper uses SVM which has the best classification performance to classify the low-dimension PolSAR data for the first time. And then, the superpixel segmentation is obtained by improving SLIC algorithm. At last, the majority voting principle is used to classify the superpixel blocks, which is the second classification and final classification of PolSAR data.

$\mathcal {H}$ Distribution for Multilook Polarimetric SAR Data

Polarimetric synthetic aperture radar (PolSAR) is an advanced imaging radar system, for which the acquired data provide not only the information of each channel but also the correlation between channels. To fully utilize and accurately model the multilook PolSAR data, a novel compound distribution, named the $\mathcal {H}$ distribution, is proposed based on the generalized Fisher distribution ( $\text{G}\mathcal {F}\text{D}$ ). Specifically, the $\text{G}\mathcal {F}\text{D}$ introduces a power parameter to the ordinary Fisher distribution. With one more free parameter, the $\text{G}\mathcal {F}\text{D}$ is flexible and versatile enough to characterize different kinds of texture. Then, by assuming the generalized-Fisher-distributed texture and the Wishart-distributed speckle, the $\mathcal {H}$ distribution is derived, whose closed-form expression is obtained with the help of Fox’s H-function. As such, the $\mathcal {H}$ distribution has a compact form and is conveniently applied to practical problems, such as modeling and classification of PolSAR data. The effectiveness of this method is tested by modeling the multilook PolSAR data and performing image classification. The experimental results demonstrate that the $\mathcal {H}$ distribution is a flexible and effective way to model multilook PolSAR data.

UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS

Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.

UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS

Abstract. Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.

Classification of Polarimetric SAR Data Based on Object-Based Multiple Classifiers for Urban Land-Cover

In this study, we used an object-oriented method for merging pixel-based classification and image segments to get an optimal classification result for an urban land-cover classification which is one of the important applications of Polarimetric SAR (PolSAR) remote-sensing. Because of the nature of PolSAR images, various features can be extracted and used in a classification. To achieve an improved classification accuracy, an optimal subset of features should be used. For this purpose, we used a class-based multiple classifier with a support vector machine (SVM) as a pixel-based classification with the class accuracy as the criterion in feature selection. We proposed the SVM margin as a new distance criterion for feature selection. In addition, to overcome the degradation of pixel-based classification results due to presence of speckle noise in PolSAR images, thematic features were used in image segmentation. In general, the proposed method consisted of three steps: feature selection, pixel-based classification, and polarimetric spatial classification. The pixel-based classification result was merged with a set of segments that were obtained from multi-resolution segmentation and the overall accuracy of the results was evaluated. The objectives of the study were to improve the accuracy of classification and to introduce margin as the new criterion for feature selection. Our results showed that the overall accuracy of the proposed method (90.07 %) was improved compared with the single SVM classifier (80.46 %) and pixel-based multiple classifier (83.61 %). Lastly, the SVM margin distance as well as class accuracy could be considered as appropriate criteria in feature selection.

A New Method for Land Cover Characterization and Classification of Polarimetric SAR Data Using Polarimetric Signatures

Conventional methods for analyzing polarimetric synthetic aperture RADAR (PolSAR) data such as scattering matrix show polarimetric information just in a restricted number of polarization bases, whereas backscattering of the targets has information on wide range of polarizations. In order to solve this problem, polarimetric signatures have been investigated to have a better illustration of the target responses. Polarimetric signatures depict more details of physical information from target backscattering in various polarization bases. This paper presents a new method for generating polarimetric signatures for different features in PolSAR data by changing the polarization basis in the covariance matrix. Furthermore, various land cover classes were evaluated using their polarimetric signatures and the pattern recognition matching methods. On the basis of this background, an object-oriented and knowledge-based classification algorithm is proposed. The main idea of this method is to apply polarimetric signatures of various PolSAR features in the land cover classification. A Radarsat-2 image, acquired in leaf-off season of the forest areas, was chosen for this study. The backscattering from different classes, including six land cover classes: 1) red oak (Or); 2) white pine (Pw); 3) black spruce (Sb); 4) urban (Ur); 5) water (Wa); and 6) ground vegetation (GV) was analyzed by the proposed method. The results reported that the polarimetric signatures of PolSAR features introduce new concepts for the various targets which are different from the polarimetric power signatures. Also, the proposed classification was compared with the object-based form of the supervised Wishart classification as the baseline method. The mean accuracy of the proposed method is 6% better than the supervised Wishart classification.

Tensorial Independent Component Analysis-Based Feature Extraction for Polarimetric SAR Data Classification

For polarimetric synthetic aperture radar (PolSAR) data, various polarimetric signatures can be obtained by target decomposition techniques, which are of great help for characterizing the land cover. It is straightforward to combine these polarimetric features together and formulate them as a third-order polarimetric feature tensor. However, how to make full use of the abundant information provided by these polarimetric features remains a challenge. A feasible solution is applying feature extraction (FE) techniques on the high-dimensional polarimetric manifold to obtain a lower dimensional intrinsic feature set. Common FE methods, such as principal component analysis (PCA), independent component analysis (ICA), etc., use matrix linear algebra and require rearranging the original tensor into a matrix. This leads to the loss of the spatial information of the PolSAR data. In this paper, to jointly take advantage of the spatial and feature information, a novel FE scheme incorporating ICA with the tensor decomposition techniques is proposed. After applying the proposed FE method on the third-order polarimetric feature tensor, each PolSAR image pixel is represented by a low-dimensional intrinsic feature vector. Furthermore, these feature vectors are fed to the $k$ -nearest neighbor (KNN) classifier and support-vector-machine classifier for supervised classification. Simulated data, together with two measured data sets, i.e., Flevoland of Airborne Synthetic Aperture Radar (AIRSAR) and Quebec City of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), are utilized to evaluate the performance of the proposed method. For comparison purpose, several classical and advanced FE methods, such as PCA, ICA, Laplacian eigenmaps, and $\hbox{LRTA}_{\rm dr}-(K_{1},K_{2},p)$ , are also applied. The experimental results demonstrate the superiority of the proposed FE method in three folds: 1) The extracted features by the proposed method are more discriminative, characterized by the high separability in the scatterplots; 2) the classification accuracy is improved as much as approximately 7% compared with the complex Wishart classifier; and 3) the proposed method is computational efficient and has fast convergence.

Polarimetric Classification of C-Band SAR Data for forest Density Characterization

Polarimetric classification is one of the most significant applications of synthetic aperture radar (SAR) remote sensing. Sensitivity of C-band SAR in discerning the variation in canopy roughness and limited penetration capability through forest canopy have been well studied at a given frequency, polarization and incidence angle. However, the scope of C-band SAR in characterizing and monitoring forest density has not been adequately understood with polarimetric techniques. The objectives of the present study were to understand the scattering behaviour of different landcover classes and evaluate the feasibility of polarimetric SAR data classification methods in forest canopy density slicing using C-band SAR data. The RADARSAT- 2 image with fine quad-pol obtained on 27 October 2011 over Madhav National Park, Madhya Pradesh, India and its surroundings was used for the analysis. Forest patches exhibit α-angle around 45°, which means the dominant scattering mechanism is volume; entropy of one or a value close to it denotes distributed targets and low anisotropy values than all other land units, which shows a dominant first scattering mechanism. This study comparatively analysed Wishart supervized classifier and Support Vector Machine (SVM) classifier for classification of the forest canopy density along with other associated land-cover classes for a better understanding of the class separability. All forest density classes showed comparatively good separability in Wishart supervized classification (73.8-84.7%) and in SVM classifier (82.3-84.8%). The results demonstrate the effectiveness of SVM classifier (88.7%) over Wishart supervized classifier (87.8%) with kappa coefficient of 0.86 and 0.85 respectively. The experimental results obtained with polarimetric C-band SAR data over dry deciduous forest area imply that SAR data have a significant potential for estimating stand density in operational forestry.