Indeed, Full Polarimetry (FP) imaging system has proven its increased potential in various applications, but suffer from an increase in the Pulse Repetition Frequency (PRF) and the data rate over single polarization. Recently, there has been growing interest in Dual Polarimetry (DP) imaging system that is called Compact Polarimetry (CP). The CP is a new mode proposed in DP system which has several important advantages in comparison with the FP mode such as reduction ability in complexity, cost, mass, and data rate of a Synthetic Aperture RADAR (SAR) system. Moreover, this new mode not only can achieve a greater amount of information than standard DP modes, but also can cover a much greater swath widths compared to FP mode. For this reason, the CP data can be critical for monitoring applications, such as forest controlling and monitoring. Forest studying is the one of research areas that is attractive for RADAR remote sensing researchers, because it has an effective role in climate controlling. Therefore, forest cover classification is essential to manage natural resources and environment, land use plans and land potential. Despite the significant number of works carried out for CP SAR applications, very few researches have been performed to investigate the capability of CP data for forest cover classification. In this paper, the potential of CP data in forest area is investigated using complex Wishart classifier in two ways. First, we use 2´2 covariance matrices of the pi/4 and Circular Transmit-Linear Receive (CTLR) CP modes simulated by RADARSAT-2 FP mode which acquired over Petawawa research forest and second, 3´3 covariance matrices reconstructed from these modes were exploited. Next, we compare the results with the FP mode. Results of this study show that the pi/4 mode provide better overall accuracy in forest cover classification than the CTLR mode, as well as extending the CTLR mode via Souyris’s iteration model to generate the PQ-CTLR mode overally does not significantly affect the Wishart classification. However, construction of the PQ mode permits a direct comparison of the average scattering mechanisms. Therefore, in the next step, the Cloude-Pottier alpha angle is considered and calculated for PQ and FP modes. The PQ_pi/4 mode shows that it is a better mode to estimate the alpha angle parameter compared to the PQ-CTLR mode. This study shows that although CP modes do not produce as good classification accuracies as produced by FP mode, they are an effective strategy when the polarimetric system resources are limited or not available. Also, they are compatible as an optional mode for a FP SAR system. Moreover, circular polarizations e.g. CTLR modes, will be less sensitive to Faraday rotation effects attached to low frequency propagation in the ionosphere. At the present, at least two earth observation satellites which provide CP modes are already in orbit or to be launched in next few years. These are Indian RISAT-1, Japanese Advanced Land Observing Satellite-2 (ALOS-2), Canadian RADARSAT Constellation Mission (RCM) and Argentina SAR Observation & Communications Satellite (SAOCOM) that will able to collect the CP data in and CTLR modes.

In the last two decades, among various Synthetic Aperture RADAR (SAR) imaging modes, Compact Polarimetry (CP) mode has come to attention due to less complex imaging system, mass and data rate reduction, and also greater swath width. Having such advantages makes this data very useful for large-scale target mapping, such as forest classification. Different methods have been proposed for forest classification using CP mode, which all of them are based on feature extraction. The accuracy of these methods depends on the discrimination of the extracted features. In the meantime, deep learning networks have almost automated the feature extraction phase and obtained impressive results, especially in the classification task. In this paper, the ability of deep learning networks using CP mode data in forest classification is investigated. The study area of this paper is Petawawa forest located in Ontario, Canada, and the data being used are simulated CP data, Full Polarimetric (FP) data, and also reconstructed Pseudo Quad (PQ) data acquired from RADARSAT-2 in C-band. The proper deep learning network for automatic feature extraction is designed and the classification is performed on CP, FP, and PQ data. The results from each mode classification are compared and evaluated with each other and also with the results from Wishart classifier and Support Vector Machine (SVM). The results of this paper show that using deep learning networks improves the classification accuracy of CP and PQ modes.

By coming the new generation of SAR polarimetric satellites, such as TerraSAR-X, RADARSAT-2, ALOS, etc., the development of polarimetric synthetic aperture radar (PolSAR) applications in the field of natural hazard and environmental, such as subsidence, soil erosion, earthquake prediction, volcano activities and flood have been accelerated. The aim of this article is extraction of basic information from POLSAR images and determining the amount of the importance of each characteristics in feature vector spaces. The elements of the feature vector space are produced by multiplication of HH, VV and HV bands that contained the scope of phase and amplitude information. Performed by making Fischer criterion for class separation, the significance of each features are verified and therefore, the features are ranked based upon the power of separability and correlations between the bands. In the next stage, by performing supervise Maximum likelihood classifier, the accuracy of the different combination of the features has been analyzed. Finally, the best combination of the existing features was obtained. Extraction of the best mining consisting of at least features in the feature vector space, not only protects the most important information, but also leads to reduction in the volume of POLSAR image processing operations. In this regard, the proposed algorithm in this article can be applied on any polarimetric data.

Nowadays، SAR imaging is a well-developed remote sensing technique for providing high spatial resolution images of the Earth’ s surface which provides a vast amount of information for environmental monitoring. Fully polarimetric (FP) SAR systems alternately transmit two orthogonal polarizations and receive the response of the scatters to each of them by two antennas with orthogonal polarizations. Transmitting two interleaved electromagnetic waves requires doubling the pulse repetition frequency which implies immediately that the image swath must be only half of the width of a single-polarized or dual-polarized SAR. In order to achieve a better swath width، and coincidentally reduce average power requirements and simplify transmitting hardware، compact polarimetric (CP) systems have been proposed with the promise of being able to maintain many capabilities of fully polarimetric systems (Souyris et al.، 2005). One of the most important CP configurations is dual circular polarimetric (DCP) mode. In order to extract the physical scattering mechanism (PSM) of targets using polarimetric data many classification methods have been presented. One of the most common such methods is H-α decomposition (Cloude and Pottier، 1998) that is proposed for FP data. Its principle relies on the analysis of eigenvalues and eigenvectors of the coherency matrix. The space of scattering entropy (H) and mean alpha angle (α ) namely H-α plane is used to classify the polarimetric image into 8 canonical PSMs. In recent years two approaches have been proposed in order to find dual H-α classification zones for DCP data. (Guo et al.، 2012) proposed an H-α classification space by mapping the points of each PSM from the original FP data into the space of H-α for CP data and subsequently (Zhang et al.، 2014) proposed an H-α space on the basis of the distribution centers and densities of different PSMs. Experimental results showed that the classification accuracy of each PSM is improved compared with the results of Guo’ s H-α space، however Zhang’ s method is not well accurate and there are still overlaps between different PSMs. The results of Zhang’ s method for H-α boundaries is highly dependent on the choice of data. For example، in one data it might exist a special class of plants that are dominant in the image and in another one another class might be dominant. So، the maximum distribution densities of these two images are different from each other. Furthermore، the specifications of different sensors are different. For example، the base noise of each sensor is different and entropy is dependent on this parameter. So، for each specific sensor its own optimum boundaries should be found. According to the fact that fully polarimetric data contains maximum polarimetric information، the efforts of the researchers in this field is to achieve the nearest information from CP data to FP data. Therefore، in this research we have found the H-α boundaries of DCP data which maximize the total class agreement of classification results of the DCP and FP data for RADARSAT-2 sensor. Two images over San Francisco and Vancouver acquired by Radarsat-2 at C-band in quad polarization mode، with the image size being 1151×1776 and 1766×1558 respectively have been used for this study. In order to evaluate the ability of the proposed H-α zones in comparison with Zhang’ s zones، Each experimental image is classified into eight PSMs. Confusion matrices have been achieved and the resultant mean agreements have been calculated. It has been shown that the proposed boundaries have increased the mean agreements of the results by 3%. In order to extract the physical scattering mechanism (PSM) of targets using polarimetric data many classification methods have been presented. One of the most common such methods is Cloude– Pottier H-α decomposition that is proposed for FP data. Its principle relies on the analysis of eigenvalues and eigenvectors of the coherency matrix. Entropy and α-angle are two important parameters for the interpretation of fully polarimetric data which are extracted from this method. They indicate the randomness of the polarisation of the back scattered waves and the scattering mechanisms of the targets respectively. For fully polarimetric data an H-α classification space has been presented. This H-α classification space is devided by H and α borders and cllassifies 8 feasible PSM regions without the need for training data. In recent years two approaches have been proposed in order to find dual H-α classification zones for DCP data. In 2012، Guo proposed an H-α classification space by mapping the points of each PSM from the original FP data into the space of H-α for DCP data and extract approximate borders. Subsequently، in 2014 Zhang proposed an H-α space on the basis of the distribution centers and densities of different PSMs. Experimental results showed that the classification accuracy of each PSM is improved compared with the results of Guo’ s H-α space، however Zhang’ s method is not well accurate and there are still overlaps between different PSMs. Both Zhang’ s and Guo’ s methods are not based on an optimization method. Therefore، they do not present optimum H-α borders for classification of DCP data. Furthermore، each sensor has its own specifications. One of which is the system noise floor which affects entropy borders for classification. Thus، it is important to find optimum H-α boundaries for each sensor separately. In this paper we have proposed a novel approach for finding optimum H/α classification borders for DCP data. The optimum borders have been found in such a way to maximize the agreement of the H-α classification results of DCP data with the H-α classification results of FP data. ‘ Mean class agreement’ is introduced and the borders which maximize this parameter have been found. The results of classification using the proposed borders have been compared with the rival method and the superiority of the proposed method has been revealed.

‫ Pycnocyla spinosa (Umbellifereae) is a wild growing plant in Isfahan, Fars, and Yazd provinces, Iran. The roots of the plant contain substantial amount of sucrose. Seasonal variation of sucrose in the plant was evaluated in order to find out the best collection time for the plant. The root of the plant was collected weekly. The sucrose content of the roots was determined using polarimetr. The results indicate that the best time of collection of P. spinosa is in July. It seems P. spinosa can be considered as a source of sucrose for further agrochemical studies.

Recently, a new mode is proposed in Dual Polarimetry (DP) imaging systems that is called Compact Polarimetry (CP) which has several important advantages in comparison with Full Polarimetry (FP) such as reduction ability in complexity, cost, mass, and data rate of a Synthetic Aperture RADAR (SAR) system. Despite these advantages, the CP mode, compared to the FP mode, still achieves less information to be extracted from targets. Therefore, accuracies of classification obtained from CP data are lower than those obtained from FP data. In this paper, a new method is proposed to improve the results of classification obtaind by using CP data. For this propose, two ways are considered. First, the CP modes simulated by RADARSAT-2 FP mode, and second, Pseudo Quad Polarimetry (PQ) modes reconstructed by exploited CP modes are combine in the extracted polarimetric feature level. Results of this study show that this combination can be increase the classification accuracies.

Performance of a micropattern gaseous detector of parallel plate type, a Micromega detector, was assessed for X-ray astronomical studies in this research. For this purpose, COMSOL Multiphysics software which solves differential equations based on Finite Element Method was used. Thus, by solving Poisson equation in the detector geometry, electric field in each point was obtained. Gain of the detector was estimated by using the obtained electric field and solving continuity equation. Results show that simulated detector gain was 100 at voltage of 400 V. Obtained results corresponds well with the results of simulation of the detector with Garfield code which verifies the performed simulation. This detector is a proper tool for X-ray Polarimetry with respect to its good characteristics such as high resolution, large area and good performance at high flux.