RADARSAT-2 Polarimetric SAR Data Research Articles

Soil Moisture Retrieval Using SAR Backscattering Ratio Method during the Crop Growing Season

Soil moisture content (SMC) is an indispensable basic element for crop growth and development in agricultural production. Obtaining accurate information on SMC in real time over large agricultural areas has important guiding significance for crop yield estimation and production management. In this study, the paper reports on the retrieval of SMC from RADARSAT-2 polarimetric SAR data. The proposed SMC retrieval algorithm includes vegetation correction based on a ratio method and roughness correction based on the optimal roughness method. Three vegetation description parameters (i.e., RVI, LAI, and NDVI) serve as vegetation descriptors in the parameterization of the algorithm. To testify the vegetation correction result of the algorithm, the water cloud model (WCM) was compared with the algorithm. The calibrated integrated equation model (CIEM) was employed to describe the backscattering from the underlying soil. To improve the accuracy of SMC retrieval, the CIEM model was optimized by using the optimal roughness parameter and the normalization method of reference incidence angle. Validation against ground measurements showed a high correlation between the measured and estimated SMC when the NDVI serves as vegetation descriptor (R2 = 0.68, RMSE = 4.15 vol.%, p < 0.01). The overall estimation performance of the proposed SMC retrieval algorithm is better than that of the WCM. It demonstrates that the proposed algorithm has an operational potential to estimate SMC over wheat fields during the growing season.

A class-based approach to classify PolSAR imagery using optimum classifier

ABSTRACT Classification of Polarimetric SAR (PolSAR) imagery is still one of the challenges in active remote sensing applications. Although a large number of features and different classifiers have been proposed, no unique approach has been found to satisfy all of the images and classes yet. In this paper, given the extracted features from PolSAR data, a new class-based feature selection (CBFS) algorithm is proposed to find the most optimum features for each class. Maximizing the discrimination of each class from the others is the main contribution of the CBFS which yields distinctive features. The selected features are then employed by classifiers to generate different classification results. Finally, a new approach is developed to combine these classification results to produce the final land cover map. Five different classifiers of Wishart Maximum Likelihood, Gaussian Maximum Likelihood, Support Vector Machine, Multi Layer Perceptron and Fuzzy Inference System are also used for classification. Given the CBFS results, two different Radarsat-2 and AirSAR PolSAR data were classified. Selected features led to improvement of about 5% in producer accuracies in comparison with two well-known Genetic Algorithm Feature Selection (GAFS) and Prototype Space Feature Selection (PSFS) methods. Moreover, Comparison results demonstrate that the fuzzy classifiers could improve the accuracies about 3% if they are suitably constructed and well designed. The achieved higher overall accuracy for the final classified map shows the effectiveness of the proposed approach over the other compared classification procedures.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Characterizing Lodging Damage in Wheat and Canola Using Radarsat-2 Polarimetric SAR Data

ABSTRACTCrops are vulnerable to strong winds, and the resulting lodging damage can have a great effect on the yield. We characterize lodged wheat and canola using polarimetric features derived from a series of polarimetric synthetic aperture radar data. Reflection asymmetry and reduced differential extinction are observed for lodged wheat, but not for canola, whose canopy structure has a high degree of randomness. The circular-pol correlation coefficient is explored as an indicator for the lodging damage of wheat. The results demonstrate that the capability of PolSAR in lodging damage monitoring is related to the canopy structure of crops.

Unsupervised polarimetric SAR urban area classification based on model-based decomposition with cross scattering

Since it has been validated that cross-polarized scattering (HV) is caused not only by vegetation but also by rotated dihedrals, in this study, we use rotated dihedral corner reflectors to form a cross scattering matrix and propose an extended four-component model-based decomposition method for PolSAR data over urban areas. Unlike other urban area decomposition techniques which need to discriminate the urban and natural areas before decomposition, this proposed method is applied on PolSAR image directly. The building orientation angle is considered in this scattering matrix, making it flexible and adaptive in the decomposition. Therefore, we can separate cross scattering of urban areas from the overall HV component. Further, the cross and helix scattering components are also compared. Then, using these decomposed scattering powers, the buildings and natural areas can be easily discriminated from each other using a simple unsupervised K-means classifier. Moreover, buildings aligned and not aligned along the radar flight direction can be also distinguished clearly. Spaceborne RADARSAT-2 and airborne AIRSAR full polarimetric SAR data are used to validate the performance of our proposed method. The cross scattering power of oriented buildings is generated, leading to a better decomposition result for urban areas with respect to other state-of-the-art urban decomposition techniques. The decomposed scattering powers significantly improve the classification accuracy for urban areas.

Radarsat-2 Polarimetric SAR Data for Boreal Forest Classification Using SVM and a Wrapper Feature Selector

The main objective is to propose a wrapper feature selection algorithm for analyzing the Radarsat-2 polarimetric SAR data for the classification of boreal forest. The method is based on the concept of feature selection and classifier ensemble. The support vector machine (SVM) algorithm is used as the classifier. The limitation of SVM as the evaluation function for feature selection is its time-consuming optimization. To accelerate the SVM training process, a training sample reduction strategy based on the notion of support vectors is proposed. Two fine quad-polarized Radarsat-2 images, which were acquired in leaf-on and leaf-off seasons, were chosen for this study. A wide range of SAR parameters were derived from each PolSAR image. A combined dataset was also considered. The classification results compared to the baseline methods demonstrate the effectiveness of the proposed wrapper scheme for forest classification.

Multitemporal polarimetric RADARSAT-2 SAR data for urban land cover mapping through a dictionary-based and a rule-based model selection in a contextual SEM algorithm

This paper presents a dictionary- and rule-based model selection approach in an adaptive contextual semi-supervised algorithm for improving urban land cover classification using high-resolution multitemporal RADARSAT-2 polarimetric SAR (PolSAR) data. Six-date PolSAR data were acquired from June to September, 2008, over the Greater Toronto Area. Contextual information and the capabilities of different PolSAR distribution models were explored by the spatially variant Finite Mixture Model (FMM) with an adaptive Markov Random Field (MRF) in a Stochastic Expectation–Maximization (SEM) algorithm. This algorithm can obtain homogenous results while preserving shape details in the complex urban environment with high accuracy. Commonly used PolSAR distribution models such as Wishart, G0p, Kp, and KummerU were compared through the proposed approaches for urban land cover mapping. According to a Goodness-of-Fit test based on Mellin transformation, an accurate PolSAR distribution model could be selected with the dictionary-based classification. However, the results showed that improvement from the dictionary-based approach was limited. Therefore, further improvements were expected by exploring expert knowledge. The initial results showed that G0p and KummerU performed better for distinguishing between low density built-up areas and forest. G0p, Kp, and KummerU are better for the low scattering classes. The Wishart model has superior capacity in separating high density built-up areas and the adjacent roads. Based on such knowledge, a set of rules was developed to integrate the advantages of alternative models. Significant improvement on the overall classification accuracy could be observed by this rule-based approach. The biggest improvement was achieved using the HD–Road rule on the G0p model with the best overall classification accuracy at 89.99% (kappa: 0.87). This represented 4.1% (kappa: 0.045) improvement over that of G0p without model selection.

AN IMPROVED SAR RADIOMETRIC TERRAIN CORRECTION METHOD AND ITS APPLICATION IN POLARIMETRIC SAR TERRAIN EFFECT REDUCTION

A new SAR radiometric terrain correction method was proposed to reduce the terrain efiects in sloped regions. Based on this method, a procedure for polarimetric SAR terrain efiect reduction was proposed, including geometric correction, shadow detection, radiometric terrain correction, and polarization orientation angle shift compensation. Experiments using RADARSAT-2 polarimetric SAR data of the Three Gorges Area, China demonstrated the efiectiveness of the proposed radiometric terrain correction method. Both visual and quantitative analyses showed that after the proposed radiometric terrain correction method was applied, the contrast between difierent slopes that caused by local incidence angle difierences, foreshortening, and layover was signiflcantly reduced. The difierence of backscattering intensity on slopes facing the radar sensor and facing away from the sensor was reduced from 12.5dB before radiometric correction to 1.3dB. The overall accuracy of land use/land cover classiflcation was improved by 11.2 percent using the terrain corrected polarimetric SAR data.

An Adaptive Contextual SEM Algorithm for Urban Land Cover Mapping Using Multitemporal High-Resolution Polarimetric SAR Data

This paper presents a semi-supervised Stochastic Expectation-Maximization (SEM) algorithm for detailed urban land cover mapping using multitemporal high-resolution polarimetric SAR (PolSAR) data. By applying an adaptive Markov Random Field (MRF) with the spatially variant Finite Mixture Model (SVFMM), spatial-temporal contextual information could be effectively explored to improve the mapping accuracy with homogenous results and preserved shape details. Further, a learning control scheme was proposed to ensure a robust semi-supervised mapping process thus avoiding the undesired class merges. Four-date RADARSAT-2 polarimetric SAR data over the Greater Toronto Area were used to evaluate the proposed method. Common PolSAR distribution models such as Wishart, G0p, Kp and KummerU were compared through this contextual SEM algorithm for detailed urban land cover mapping. Comparisons with Support Vector Machine (SVM) were also conducted to assess the potential of our parametric approach. The results show that the Kp, G0p and KummerU models could generate better urban land cover mapping results than the Wishart model and SVM.

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.