polarimetric SAR (PolSAR)

Top PDF polarimetric SAR (PolSAR):

Edge Detection in Polarimetric SAR Image based on Bandelet Transform

Edge Detection in Polarimetric SAR Image based on Bandelet Transform

Edge detection is critical for various synthetic aperture radar applications such as image segmentation, detection of features like roads, coastline, crop area, forest cover and so on. But speckle noise is an issue which plagues polarimetric SAR images and is worsened by its multiplicative nature which induces high probability of false alarm. Sliding window techniques using the ratio of the averages are the norm when it comes to conventional edge detectors [2], [3]. These detectors are simple to implement, however, because of their dependence on window size being selected, they are not immune to noise. Though multiscale analysis tools have been developed for edge detection which could counter many of these issues [4]–[7]. But when it comes to fully polarimetric SAR images, the research efforts on this front have found to be lacking. PolSAR could improve the efficiency of edge detection as compared to a single channel SAR [8],[9]. Majority of the edge detection techniques, when it comes to
Show more

6 Read more

Application Of Polarimetric SAR For Surface Parameter Inversion And Land Cover Mapping Over Agricultural Areas

Application Of Polarimetric SAR For Surface Parameter Inversion And Land Cover Mapping Over Agricultural Areas

Remote sensing is a spatial science used to obtain information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomena under investigation (Lillesand et al., 2004). Remote sensing technology has the potential to instantaneously provide quantitative information on agricultural crops over large areas repetitively (Clevers et al., 1994). However, the usability of different optical sensors for determining environmental conditions and crop variables depends not only on daylight, but also on the actual weather conditions. Clouds and heavy rain are impenetrable for the visible spectrum with the wavelength between 400 nm and 700 nm. Infrared sensors that are applicable during the day and night are even more sensitive to weather conditions. Synthetic Aperture Radar (SAR) as an active observation technique, can transmit longer electromagnetic wavelengths from 1 mm to 1 m and receive the scattered waves after interacting with the ground targets, having proved to be valuable because of its day-and-night capability and the possibility to penetrate clouds and light rain (Berens, 2006). Of increasing importance are SAR systems that can provide multidimensional information via multiple frequencies or polarizations. One such technique is the polarimetric SAR (PolSAR) with its definition given in Appendix A, which provides an enhanced capacity for investigating Earth terrain because different frequencies and polarizations allow for the probing of different scattering mechanisms and different components of the scattering layers (Oliver et al., 2004). Compared with the single polarization SAR, PolSAR with quad polarizations is more sensitive to crop geometric structures from which the radar signal returns and has been extensively used for the land use and land cover mapping (Liu et al., 2013; Jiao et al., 2013).
Show more

264 Read more

An eigenvalue-based approach for structure classification in polarimetric SAR images

An eigenvalue-based approach for structure classification in polarimetric SAR images

Polarimetric Synthetic Aperture Radar imaging (PolSAR) has been demonstrated to have the capability to provide highly reliable and valuable information for remote sensing [2]–[5], allowing advanced discrimination and understanding of the imaged scene. This radar imaging sensor acquires information from a scene when vertical or horizontal polarization is trans- mitted and/or received. Scattering mechanisms that rule the de-polarization effect can be then identified and used to infer about the observed scene. The exploitation of SAR polarimetry is of particular relevance in civilian and defence applications. In this former context, extended area monitoring and target areas classification receive the widest attention by researchers. A non-exhaustive list of applications of polarimetric SAR in remote sensing includes biomass estimation [6], rice paddy monitoring [7], snow and ice analysis [8], oil spill detec- tion [4], land-use classification [9], crop monitoring, damage assessment, deforestation, flood delineation, burn mapping, disaster management, urban mapping and others [10].
Show more

5 Read more

Estimation of the Degree of Polarization in Polarimetric SAR Imagery : Principles and Applications

Estimation of the Degree of Polarization in Polarimetric SAR Imagery : Principles and Applications

T h e r e exists a variety of polarimetric SAR imaging modes; tra- ditional ones are linear single- and dual-pol modes. More sophisticated ones are (linear/hybrid) full-polarimetric modes. Other alternative modes, such as hybrid and compact dual-pol, are also recently proposed for future SAR missions. The discussion is vivid across the whole remote sensing society about both the utility of such alternative modes, and also the trade-off between dual and full po- larimetry. The discussion is particularly active on two distinct levels; the applications, and the system design. This thesis contributes to that discussion by analyzing and comparing different modes of operation in a variety of applications. To that end, we first briefly present these polarimetric SAR modes in § 2 . 1 and § 2 . 2 . On the other hand, estab- lishing a comprehensive PolSAR database is an important first step, and a challenging process, for our analysis and comparison. In § 2 . 3 , we introduce the data and study sites used throughout this thesis. This database has been composed based on publicly available data from a variety of organizations, in particular, NASA / JPL , NASA / GSFC , ASF ,
Show more

138 Read more

Urban Area Extraction Using X-Band Fully Polarimetric SAR Imagery

Urban Area Extraction Using X-Band Fully Polarimetric SAR Imagery

Following these works, several improvements were achieved via analysis. Chen et al. [9] showed that the POA effect in scattering for large POA may not be corrected even after deorientation. This is because model-based decomposition assumes that only volume scattering contributes to the cross-polarization term. We also reported the results of experiments performed in an anechoic room, which showed that the total powers of backscattering and the four components derived from fully polarimetric scattering are highly dependent on the orientation angles [10]. With these findings, we have already reported a method for extracting urban areas by using Advanced Land Observing Satellite (ALOS) / Phased Array type L-band Synthetic Aperture Rader (PALSAR) imagery [10] and another method for estimating urban densities by using a single fully polarimetric SAR (PolSAR) image [11,12].
Show more

13 Read more

A learning-based target decomposition method using Kernel KSVD for polarimetric SAR image classification

A learning-based target decomposition method using Kernel KSVD for polarimetric SAR image classification

Synthetic Aperture Radar (SAR)[1] has become an impor- tant tool for a wide range of applications, including in military exploration, resource exploration, urban devel- opment planning and marine research. Compared with single-polarized SAR, polarimetric SAR (PolSAR) can work under different polarimetric combinations of trans- mitting and receiving antennas. Since combinations of electromagnetic waves from antennas are sensitive to the dielectric constant, physical characteristics and geometric shape, PolSAR gives birth to a remarkable enhancement on capabilities of data application and obtains rich target information with identification and separation of full- polarized scattering mechanisms. As an important com- ponent of PolSAR image interpretation, target decom- position[2] expresses the average mechanism as the sum
Show more

9 Read more

Optimum Ensemble Classification for Fully Polarimetric SAR Data Using Global-Local Classification Approach

Optimum Ensemble Classification for Fully Polarimetric SAR Data Using Global-Local Classification Approach

In the first step, the training and test sets are formed. Figure 1 represents this process. Firstly, while surveying overall display of the image, a desired area is selected using PolSARpro software, and the coherency matrix of the pixels in this area is extracted. Depending on the land cover of the selected area, C classes are considered. The problem of speckle as one of the main issues in polarimetric SAR data complicates the image analysis and reduces the effectiveness of image segmentation and classification [20]. Thus, the speckle reduction is a fundamental step prior to extracting valuable parameters of PolSAR data. Several methods such as Multi-look processing, Lee filter, refined Lee filter and IDAN filter have been proposed to reduce speckle [21]. These methods attempt to find a good compromise between speckle reduction and preservation of spatial details.
Show more

8 Read more

Analysis of scattering components from fully polarimetric SAR images for improving accuracies of urban density estimation

Analysis of scattering components from fully polarimetric SAR images for improving accuracies of urban density estimation

In 2006, the Japan Aerospace Exploration Agency (JAXA) launched the first satellite-borne fully polarimetric SAR (PolSAR) sensor, the Advanced Land Observing Satellite (ALOS) / Phased Array type L-band SAR (PALSAR). This success promoted research using PolSAR images. From these data, urban areas are easily extracted by lessening the effects of the orientation angles of ob- jects against the radar beam (Kajimoto and Susaki, 2013a). The method utilizes volumetric scattering derived from four-component decomposition methods (Yamaguchi et al., 2011). In addition, a method was presented that estimates urban density from a fully PolSAR image (Kajimoto and Susaki, 2013b; Susaki et al., 2014). The method uses an index T v+c obtained by normalizing the sum
Show more

9 Read more

Unsupervised Classification of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features

Unsupervised Classification of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features

Some classification methods of polarimetric SAR image are studied based on features of polarimetric SAR at home and abroad. According to the characteristics of fully polarimetric SAR data, extracting features containing polarimetric information based on the data distribution characteristics or scattering mechanism, classification methods are designed to complete the terrain classification. This kind of methods can be probably subdivided into the following kinds of classification methods, the first kind of methods are those which are based on the statistical characteristics of polarimetric SAR, and the second kind of methods are those which are based on the polarimetric SAR scattering mechanism. In fully polarimetric SAR image classification based on statistical distribution and scattering mechanism, the typical polarimetric SAR image classification methods include, the multi-polarization SAR image classification method based on Wishart distribution, the unsupervised SAR image classification method based on H/α target decomposition, and the unsupervised polarimetric SAR image classification method based on Freeman decomposition,etc. In the study of specific polarimetric SAR image classification, some methods are proposed. Combined with H/α/A and maximum likelihood estimation based on complex Wishart disrtribution, Pottier proposed the Wishart-H/α/A unsupervised classification method [1], which is currently the most widely used in fully polarimetric SAR data classification at present. Lamei Zhang, etc., on the basis of the sparse characteristics of the features for PolSAR image classification, proposed a supervised PolSAR image classification method based on sparse representation. First, the effective features are extracted to describe the distinction of each class. Then, the feature vectors of the training samples construct an over-complete dictionary and obtain the corresponding sparse coefficients; meanwhile, the residual error of the pending
Show more

8 Read more

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

Crops play an essential role in global economic activity, diets, biofuel and climate change [1]. Mapping the distribution and changes of cropland area can provide useful information to support the sustainable management and development of agriculture [2]. To obtain this vital information, a comprehensive, systematic and accurate global or regional monitoring technology is required [2,3]. Remote sensing has shown strong ability to observe the land in high spatial and spectral resolution, wide-range cover with a short revisit time. It has incomparable advantages with respect to the traditional field measurements and is developing rapidly to support this growing demand. In particular, synthetic aperture radar (SAR), an active microwave remote sensing technology, has the capability of operation in all-time and all-weather. It is crucial for global agriculture monitoring, especially, in case the optical sensors are limited to work, such as with persistent cloud cover, haze and none solar illumination [3]. In addition, the SAR images can provide unique structural characteristics of vegetation with respect to the optical image. For crops, radar backscatter is influenced by many factors, such as the shape and structural attributes of crops, the dielectric properties of the crop canopy and the underlying background soil, the planting density and row direction, etc. [2]. With respect to single or dual-polarization SAR, Polarimetric SAR (PolSAR) can provide richer information and sensitivity to the types of scattering mechanisms present in the scene [4,5]. Since radar response to crops is polarization dependent, the exploitation of differences in the polarization signatures for crop classification can achieve improvements of accuracy with respect to single polarization SAR data [4–6]. A number of PolSAR satellite sensors have been launched in past years, such as RADARSAT-1/2 in Canada, ALOS-1/2 in Japan, TerraSAR-X and Tandem-X in Germany, GF-3 in China, RISAT-1/2 in India, SAOCOM-1/2 in Argentina, etc. In addition, some airborne PolSAR platforms have been developed as well. Based on these abundant PolSAR datasets, plenty of studies employing polarimetric features with single/multi-temporal data for crop classification have been reported in the literature [6–21].
Show more

20 Read more

Classification of covariance matrix eigenvalues in polarimetric SAR for environmental monitoring applications

Classification of covariance matrix eigenvalues in polarimetric SAR for environmental monitoring applications

In this paper, we describe novel techniques for automatic classification of the dominant scattering mechanisms associated with the pixels of polarimetric SAR images. Specifically, we investigate two operating scenarios. In the first scenario, it is assumed that the polarimetric image pixels locally share the same covariance (homogeneous environment), whereas the second scenario considers polarimetric pixels with different power levels and the same covariance structure (heterogeneous environment). In the second case, we invoke the Principle of Invariance to get rid of the dependence on the power levels. For both scenarios, we formulate the classification problem in terms of multiple hypothesis tests which is addressed by applying the model order selection rules. The performance analysis is conducted on both simulated and measured data and demonstrates the effectiveness of the proposed approach.
Show more

25 Read more

Urban density mapping of global megacities from polarimetric SAR images

Urban density mapping of global megacities from polarimetric SAR images

 We estimated urban areas and density from a single polarimetric SAR image.  We calculated statistics from images to reduce orientation angle effects.  The estimated urban density has a high correlation with building-to-land ratio.  We compared the urban density patterns of global megacities.

93 Read more

A Novel Decision Tree Algorithm for Land Cover Classification Using Hybrid Polarimetric Sar Data

A Novel Decision Tree Algorithm for Land Cover Classification Using Hybrid Polarimetric Sar Data

The polarimetric information contained in polarimetric synthetic aperture radar (SAR) images represents great potential for characterization of natural and urban surfaces. However, it is still challenging to identify different land cover classes with polarimetric data. Hybrid polarimetric SAR data (RH, RV) from RISAT - 1 is found to be suitable for land cover classification of significant features that are well distinguished. The availability of high resolution hybrid polarimetric data from RISAT - 1 SAR systems supporting FRS -1 mode, made it possible to analyze the scattering mechanism for different land use and land cover features using the Raney decomposition (m- alpha, m-chi, and m-delta) techniques. Further to perform both supervised classification(parallelepiped, minimum distance, maximum likelihood and isodata classifiers) and machine learning (artificial neural net) classification also performed Decision tree classification.The proposed statistical Gumbel distribution model has been implemented and retrieves the threshold intensity values. In this proposedwork classification approach has been evaluated for RISAT-1 SAR hybrid polarimetric data of 21 st October 2014 over an urban city, Visakhapatnam, in the state of Andhra Pradesh, India. Since the hybrid polarimetric radar data contains all the scattering information for any arbitrary polarization state, data of any combination of transmitting and receive polarizations can be synthesized, mathematically from hybrid polarimetric data. The RISAT-1 SAR hybrid polarimetric data were decomposed to retrieve the surface and volume scattering information. Both supervised classification and machine learning classification methods were appliedto land cover and few other land use classes based on ground truth measurements using maximum-likelihood (ML) distance measures that are derived from the complex distribution of SAR data at various polarization combinations. The results show that Decision tree classification accuracies for m- alpha, m-chi and m-delta methods were 99.743, 96.873 and 99.857 respectively. RISAT-1 hybrid polarimetric SAR data helps to classify land cover features efficiently.
Show more

12 Read more

ANALYSIS OF X-BAND POLARIMETRIC SAR DATA FOR THE DERIVATION OF THE SURFACE ROUGHNESS OVER BARE AGRICULTURAL FIELDS

ANALYSIS OF X-BAND POLARIMETRIC SAR DATA FOR THE DERIVATION OF THE SURFACE ROUGHNESS OVER BARE AGRICULTURAL FIELDS

The potential of polarimetric SAR data at X-band has been tested with data acquired with the ONERA’s RAMSES system over an area of Avignon in Southern France. The polarimetric measurements provide a more complete description of targets than is possible with a single-channel radar system. Results obtained using the backscattering coefficients and the polarimetric parameters calculated from the eigen decompostion of the coherency matrix show moderate discrimination between classes at X-band. Certain classes, wheat, lawn and orchards for example, are difficult to classify. The radar signal at X-band acquired at an incidence angle of around 26° is weakly correlated to the surface roughness over bare soils. However, it is possible to observe the surface degradation due to the slaking process and to distinguish the freshly tilled fields.
Show more

6 Read more

Urban-Area Extraction From Polarimetric SAR Images Using Polarization Orientation Angle

Urban-Area Extraction From Polarimetric SAR Images Using Polarization Orientation Angle

The polarization orientation angle (POA) [1] can be used to reduce this dependence. Applying a four-component decomposition method [2], [3] to the fully polarimetric SAR data gives the surface scattering power (Ps), the double-bounce scattering power (Pd), the volume scattering power (Pv), and the helix scattering power (Pc); however, these components are also sensitive to POA. Thus, Yamaguchi et al. [4] proposed an algorithm that rotates the coherency matrix by the POA in order to reduce the dependence of the components on the relative azimuth. However, the dependence was found to remain even after this correction [5], and removing the remaining angular effects is considered nontrivial. Therefore, in this paper, we propose an algorithm for extracting urban areas from data containing these angular effects. The remainder of the paper is organized as follows. Section II explains the indices used in the proposed algorithm. The algorithm itself is then described in Section III, and experimental results are reported and discussed in Section IV. Finally, the paper is concluded in Section V.
Show more

6 Read more

Automatic GCP Extraction of Fully Polarimetric SAR Images

Automatic GCP Extraction of Fully Polarimetric SAR Images

When comparing the results for the four areas, no differences were found. However, there are certain factors that affect the accuracy, such as the size of the images and the characteristics of the topography. To evaluate the changes in accuracy resulting from different image sizes, the different sizes of the same SAR images were used (first with the original images and then with images compressed using a 1/16 azimuth compression ratio). This was done by applying the adapted SIFT-OCT algorithm to the polarimetric SAR imagery, as described in Section II A. The comparative results are in Table 2 and Fig. 4, respectively. These results shows that the application of the SIFT-OCT algorithm (reduction in size) entails higher accuracy at all steps and reduces the processing time. Considering the performance in the case of the SIFT algorithm, Schwind et al. [1], [2], found that the majority of false matches occurred at the first octave (highest scale) of the scale space pyramid. Furthermore, the presence of speckle noise in SAR images obstructs accurate detection. For this reason, the GCP matching accuracy was poor in this case. On the other hand, considering the case of the SIFT-OCT algorithm for polarimetric SAR images compressed using a 1/16 azimuth compression ratio (Fig. 4), more robust keypoints can be found at the lower scale level due to the reduced influence of speckle noise in all the octaves with the compressed images. For this reason, accurate matches are selected, which results in high GCP matching accuracy. It can thus be seen that the SIFT-OCT algorithm can be effective in obtaining higher performance in GCP extraction of polarimetric SAR images.
Show more

21 Read more

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Radarsat-2 is a C-band (5.3 GHz) polarimetric SAR system with the spatial resolutions varying from 3 to 100 meters (Appendix A). Although the orbit repeat cycle is 24 days, the flexibility of the steerable radar beam makes the revisit intervals shorter. With the available of quad-polarization data from satellites such as Radarsat-2, it is possible to study the sensitivity of more polarimetric SAR parameters including the four polarizations (HH, HV, VH, and VV) and several decompositions extracted from the quad polarization scattering matrix. Previous studies have investigated the sensitivity of SAR parameters to crop biophysical variables such as LAI and biomass, and it was observed that the responses of SAR backscatter to LAI or biomass of narrow-leaf crops such as wheat were different from the responses of SAR backscatter to the LAI or biomass of broad-leaf crops such as corn (Fontanelli, Paloscia, Zribi, & Chahbi, 2013; Macelloni, Paloscia, Pampaloni, Marliani, & Gai, 2001; Mattia et al., 2003; Smith et al., 2006; Wiseman et al., 2014). Besides LAI and biomass, crop height is an important crop variable in vegetation growth dynamics monitoring, and FVC has been used for crop biomass estimation and crop evapotranspiration modeling (Paruelo, Lauenroth, & Roset, 2000; Singh, Dutta, & Dharaiya, 2013). If these crop variables can be estimated from Radarsat-2 polarimetric SAR data, the high temporal frequency requirement can be met. However, the responses of Radarsat-2 polarimetric SAR backscatter to crop height and FVC were not well documented in the literature. In order to investigate the potential of Radarsat-2 polarimetric SAR in crop height and crop FVC estimation and monitoring, the objectives of this study are (1) to investigate the sensitivity of different Radarsat-2 polarimetric SAR parameters to crop height and FVC of corn and wheat, and (2) to explore the variations in SAR responses to crop height and FVC at different crop growth stage.
Show more

235 Read more

Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests

Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests

Copyright © 2010 Tongyuan Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Terrain classification using polarimetric SAR imagery has been a very active research field over recent years. Although lots of features have been proposed and many classifiers have been employed, there are few works on comparing these features and their combination with different classifiers. In this paper, we firstly evaluate and compare different features for classifying polarimetric SAR imagery. Then, we propose two strategies for feature combination: manual selection according to heuristic rules and automatic combination based on a simple but efficient criterion. Finally, we introduce extremely randomized clustering forests (ERCFs) to polarimetric SAR image classification and compare it with other competitive classifiers. Experiments on ALOS PALSAR image validate the effectiveness of the feature combination strategies and also show that ERCFs achieves competitive performance with other widely used classifiers while costing much less training and testing time.
Show more

9 Read more

Detecting covariance symmetries for classification of polarimetric SAR images

Detecting covariance symmetries for classification of polarimetric SAR images

pabilities of the entire system. Among the different techniques available in open literature [1], a possible approach is to take advantage of the full-polarimetric data extracting for each pixel of the considered scene the polarimetric covariance matrix, coherence matrix, Muller matrix, and so on [1]–[4], and to use them in order to achieve a specific objective. Usually, the quantity measured by a polarimetric radar is the well- known scattering matrix [3] (also called the Sinclair matrix [1, p. 63]); however, it is very useful to express the latter in a vectorized form and compute some second-order moment- based metrics, i.e., covariance and coherence matrices, that can be utilized to have inference about the scattering mech- anisms characterizing the objects in the scene of interest. Moreover, a widely accepted processing strategy to deal with polarimetric SAR images relies on the coherent decomposi- tion of the polarimetric scattering matrix. In this context, the Pauli [5], Krogager [6], and Cameron [7] decompositions play a central role. The aim of all these decompositions is to rep- resent the scattering matrix as a combination of the scattering responses of independent elements (for instance, single/odd- bounce scattering and double/even-bounce scattering), to asso- ciate a physical mechanism with each component and to extract relevant characteristics from polarimetric data sets.
Show more

16 Read more

A multi-family GLRT for detection in polarimetric SAR images

A multi-family GLRT for detection in polarimetric SAR images

Polarimetric SAR images provide enhanced information on the imaged scene that can be exploited for improved target de- tection, recognition and scene classification [1]. Following the imaging stage, target detection can be applied and improved performance are achievable exploiting the multi-polarimetric nature of the data. Detectors exploiting the polarimetric infor- mation have been developed for specific applications including change [2], [3], [4], oil spill [5], [6] and ship detection [7]. In this paper, the problem of target detection is formulated in terms of a binary hypothesis test aimed at discriminating between the presence and the absence of variations in the Polarimetric Covariance Matrix (PCM) of the radar returns. The presence of targets such as oil spills and ship wakes modifies the backscattering of sea surface. The idea is to compare the region under test, which possibly contains targets, to a reference area where only echoes from the sea are present. Without loss of generality, in this paper we will
Show more

5 Read more

Show all 799 documents...