Qihao Chen

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. A novel supervised superpixel-based classification method is proposed in this study to suppress the influence of speckle noise on PolSAR images for the purpose of obtaining accurate and consistent classification results. This method combines statistical information with spatial context information based on the stochastic expectation maximization (SEM) algorithm. First, a modified simple linear iterative clustering (SLIC) algorithm is utilized to generate superpixels as classification elements. Second, class posterior probabilities of superpixels are calculated by a K distribution in iterations of SEM. Then, a neighborhood function is defined to express the spatial relationship among adjacent superpixels quantitatively, and the class posterior probabilities are updated by this predefined neighborhood function in a probabilistic label relaxation (PLR) procedure. The final classification result is obtained by the maximum a posteriori decision rule. A simulated image, a spaceborne RADARSAT-2 image, and an airborne AIRSAR image are used to evaluate the proposed method, and the classification accuracy of our proposed method is 99.28%, 93.16% and 89.70%, respectively. The experimental results indicate that the proposed method obtains more accurate and consistent results than other methods.

Adaptive Coherency Matrix Estimation for Polarimetric SAR Imagery Based on Local Heterogeneity Coefficients

Polarimetric synthetic aperture radar (SAR) images usually contain a mixture of homogeneous and heterogeneous regions, which makes estimation of the coherency matrix a very challenging task. In this paper, we propose an adaptive coherency matrix estimation method that employs local heterogeneity coefficient and leverages the sample covariance matrix estimation to the homogeneous components and the fixed-point estimation to the heterogeneous components. Evaluations were conducted with synthetic polarimetric data and real-world SAR imagery, including UAVSAR, RADARSAT-2, and ESAR. Our experimental results demonstrated that the heterogeneity coefficient effectively characterizes the scattering property of ground objects, which enables adaptive estimation of the coherency matrix in high-resolution polarimetric SAR imagery. Our method was able to handle single- and multilook polarimetric SAR imagery gracefully. Compared with the sample covariance matrix estimator, the fixed-point estimator, and the Lee sigma filtering, our method achieved the best performance for retaining the spatial structure, suppressing speckles, and preserving polarimetric information of SAR imagery with different degrees of heterogeneity.

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of high-resolution PolSAR image segmentation. A novel segmentation algorithm is proposed in this study in order to address the problem and to obtain accurate and precise segmentation results. This method integrates statistical features into a fractal net evolution algorithm (FNEA) framework, and incorporates polarimetric features into a simple linear iterative clustering (SLIC) superpixel generation algorithm. First, spectral heterogeneity in the traditional FNEA is substituted by the G0 distribution statistical heterogeneity in order to combine the shape and statistical features of PolSAR data. The statistical heterogeneity between two adjacent image objects is measured using a log likelihood function. Second, a modified SLIC algorithm is utilized to generate compact superpixels as the initial samples for the G0 statistical model, which substitutes the polarimetric distance of the Pauli RGB composition for the CIELAB color distance. The segmentation results were obtained by weighting the G0 statistical feature and the shape features, based on the FNEA framework. The validity and applicability of the proposed method was verified with extensive experiments on simulated data and three real-world high-resolution PolSAR images from airborne multi-look ESAR, spaceborne single-look RADARSAT-2, and multi-look TerraSAR-X data sets. The experimental results indicate that the proposed method obtains more accurate and precise segmentation results than the other methods for high-resolution PolSAR images.

Building collapse extraction using modified freeman decomposition from post-disaster polarimetric SAR image

It is still a challenge to obtain the collapsed building distribution from post-disaster polarimetric synthetic aperture radar (SAR) data. This paper proposed a novel approach for extracting the spatial distribution of collapsed buildings using post-disaster RADARSAT-2 SAR data. In this method, non-building areas are removed by using eigen-values λ2 + λ3. Then, the modified Freeman decomposition which includes deorientation selectively and surface scattering characteristic parameter constraint is presented for building area. The contribution of the double-bounce component (PD/span) is used to extract the collapsed building spatial distribution. The method was tested on RADARSAT-2 fine-mode polarimetric SAR imagery from the Yushu earthquake which acquired on April 21, 2010. By comparison with other methods, the results confirm the validity of the proposed method.

Building damage assessment from PolSAR data using texture parameters of statistical model

Abstract Accurate building damage assessment is essential in providing decision support for disaster relief and reconstruction. Polarimetric synthetic aperture radar (PolSAR) has become one of the most effective means of building damage assessment, due to its all-day/all-weather ability and richer backscatter information of targets. However, intact buildings that are not parallel to the SAR flight pass (termed oriented buildings) and collapsed buildings share similar scattering mechanisms, both of which are dominated by volume scattering. This characteristic always leads to misjudgments between assessments of collapsed buildings and oriented buildings from PolSAR data. Because the collapsed buildings and the intact buildings (whether oriented or parallel buildings) have different textures, a novel building damage assessment method is proposed in this study to address this problem by introducing texture parameters of statistical models. First, the logarithms of the estimated texture parameters of different statistical models are taken as a new texture feature to describe the collapse of the buildings. Second, the collapsed buildings and intact buildings are distinguished using an appropriate threshold. Then, the building blocks are classified into three levels based on the building block collapse rate. Moreover, this paper also discusses the capability for performing damage assessment using texture parameters from different statistical models or using different estimators. The RADARSAT-2 and ALOS-1 PolSAR images are used to present and analyze the performance of the proposed method. The results show that using the texture parameters avoids the problem of confusing collapsed and oriented buildings and improves the assessment accuracy. The results assessed by using the K/G0 distribution texture parameters estimated based on the second moment obtain the highest extraction accuracies. For the RADARSAT-2 and ALOS-1 data, the overall accuracy (OA) for these three types of buildings is 73.39% and 68.45%, respectively.

Superpixel-based classification using semantic information for polarimetric SAR imagery

Polarimetric SAR classification is an effective approach in image understanding. This paper proposes a novel semantic method for classification of Polarimetric SAR data. The method combines superpixels and semantic model to benefit from both the object-oriented classification and the high-level semantic information. Firstly, pixels was grouped into superpixels via Simple Linear Iterative Clustering (SLIC). Secondly, the feature vector was generated within the superpixels by considering both polarimetric information and textures. To incorporate semantic information, the feature vectors were further processed via probabilistic Latent Semantic Analysis (pLSA). Finally, Supporting Vector Machine (SVM) was utilized to obtain classification results. The results were evaluated with respect to the accuracy of classification and spatial preservation. The results of this work were analyzed by means of RADARSAT-2 data.

Feature-Based Nonlocal Polarimetric SAR Filtering

Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance ( CV ) and Pauli basis (PB) to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

Building damage assessment of compact polarimetric SAR using statistical model texture parameter

Accurate building damage assessment is essential to providing decision support for disaster relief and reconstruction. This paper presents a method for building damage assessment using the statistical model texture parameter (SMTP) of compact polarimetric (CP) synthetic aperture radar (SAR). This paper uses the SMTP of CP data to describe the homogeneity of building, and using threshold to extract the collapsed buildings. Then the extent of the damage buildings is evaluated based on the proportion of collapsed buildings in the block. The C band, RADARSAT-2 data is used to present and analyze the performance of the proposed method. Compared with other method, the assessment accuracy is improved. The F1-score (harmonic mean of detection rate and false alarm rate) of serious/moderate/slight damage are 78.37%, 59.30% and 72.48%, respectively, and the overall accuracy is 71.99%.

Polarimetric SAR images classification based on L distribution and spatial context

To obtain accurate classification results of polarimetric SAR images in different heterogeneity areas, a novel unsupervised classification method is proposed, which combines an advanced distribution with spatial contextual information based on stochastic expectation maximization (SEM) algorithm. Specifically, the probabilities of class membership are calculated by L distribution, and a neighborhood function is defined to describe spatial contextual information. Then the probabilities of class membership are altered by the predefined neighborhood function via probabilistic label relaxation (PLR) technique. Moreover, RADARSAT-2 and EMISAR data are used to verify the effectiveness of the proposed method. The experiment results show it can accurately classify different heterogeneity areas and obtain more consistent results compared with other algorithms.

Evaluation of entropy/alpha/anisotropy based on adaptive coherency matrix estimation

Entropy, alpha, and anisotropy (H/α̅/A) of Cloude decomposition are effective in polarimetric SAR image understanding and geophysical information inversion. As an incoherent target decomposition, the inner sample covariance matrix estimation severely affects the estimated parameters. The contradiction between details preservation and accurate parameters estimation is still a challenge task. In this article, we propose adaptive coherency matrix estimation based on local heterogeneity coefficients, and utilize it to parameters estimation of Cloude decomposition. The results were evaluated with respect to details preservation and the accuracy of parameters estimation. The results of this work were analyzed by means of AIRSAR data.