Land cover maps derived from satellite images play a key role in regional and national land cover assessments. In order to compare maximum likelihood and minimum distance to mean classifiers, LISS-III images from IRS-P6 satellite were acquired in August 2008 from the western part of Isfahan. First, the LISS-III image was georeferenced. The Root Mean Square error of less than one pixel was the result of registration. After creating false color composite and calculating transformed divergence index, the images were classified using maximum likelihood and minimum distance to mean classifiers into six categories including river, bare land, agricultural land, urban area, highway and rocky outcrops. The results of classification showed that the dominant land cover type is urban area, occupying about 6821.1 ha representing 38.86% of total area. The accuracy of maximum likelihood and minimum distance to mean classifiers was obtained using error matrix and Kappa analysis. According to the results, the maximum likelihood algorithm had an overall accuracy of 94.93% and the minimum distance to mean method was 85.25% accurate. The results illustrate that the maximum likelihood method is superior to minimum distance to mean classifier.

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.

A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed classifier can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not has any over-fitting and over-learning problems.Other swarm intelligence based classifiers do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem.The experimental results show that the proposed multi-objective classifier can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices.

In this paper, the problem of classification of motor imagery EEG signals using a sparse representation-based classifier is considered. Designing a powerful dictionary matrix, i.e. extracting proper features, is an important issue in such a classifier. Due to its high performance, the Common Spatial Patterns (CSP) algorithm is widely used for this purpose in the BCI systems. The main disadvantages of the CSP algorithm are its sensibility to noise and the over learning phenomena when the number of training samples is limited. In this study, to overcome these problems, two modified form of the CSP algorithms, namely the DLRCSP and GLRCSP have been used. Using the adopted methods, the average detection rate is increased by a factor of about 7.78 %. Also, a problem of the SRC classifier which uses the standard BP algorithm is the computational complexity of the BP algorithm. To overcome this weakness, we used a new algorithm which is called the SL0 algorithm. Our classification results show that using the SL0 algorithm, the classification process is highly speeded up. Moreover, it leads to an increase of about 1.61% in average correct detection compared to the basic standard algorithm.

بنابراین تلفیق روش های مهندسی آینده پژوهی ازجمله روش سناریونویسی(جی بی ان) و مکتور به عنوان راه کاری برای طبقه بندی با این وجود، حجم سرمایه گذاری اتحادیه در مناطق مختلف جهان، توسعه فناوری های کلیدی، افزایش مناسبات اقتصادی با سایر کنشگران نظام بین الملل و نفوذ این سازمان در مناطق سرشار از منابع گازی، طلا و... ازجمله آفریقا و اوراسیا، روند افزایش قدرت اتحادیه در همه ابعاد به خصوص اقتصاد سیاسی جهانی را نمایان می سازد. فهم دوگانه موجود از جایگاه آینده اتحادیه اروپا در اقتصاد سیاسی جهانی، لزوم نگارش یک مقاله که ترسیم کننده سناریوهای مشخصی برای اتحادیه اروپا باشد را مطرح ساخت. ترسیم سناریوها صرفاً از طریق مطالعه اسنادی و کتابخانه ای میسر نبود. و تولید داده جدید در این حوزه، مورد توجه قرار گرفت. به علاوه تلاش شد دیده بانی دقیقی در اسناد موجود در اندیشکده های مختلف صورت پذیرد و با هماهنگی مراکز مختلف، زمینه برای برگزاری پنل های خبرگی، جلسات طوفان فکری و همین طور استفاده از نرم افزار مکتور فراهم شود. در رابطه با ساماندهی مقاله، این نکته شایان ذکر است که در این مقاله ابتدا به چارچوب نظری(نهادگرایی) پرداخته شده و در گام بعد بر اساس روش مکتور مهم ترین کنشگران شناسایی شده اند و در گام سوم بر اساس روش جی بی ان، کنشگران، فاکتورهای کلیدی، پیشران ها،عدم قطعیت ها و نهایتاً سناریوها استخراج شده اند.

In this study, we used an object-oriented method for merging pixel-based classification and image segments to get an optimal classification result. Urban land-cover classification is one of the important applications in polarimetric SAR remote-sensing images. Because of the nature of PolSAR images, many features can be extracted and used for classification. To achieve classification accuracy, optimal subset of features should be used. For this purpose, we used a class-based multiple classifier with SVM as a pixel-based classifier with class accuracy as a criterion in feature selection. Also we used random feature selection for create multi-classifiers. In addition, because of speckle noise in PolSAR images, pixel-based classification result may not be satisfactory. Thematic features used in image segmentation can be helpful to solve this problem. In general, the proposed method has three steps: feature selection, pixel-based classification, and polarimetric spatial classification. The pixel-based classification result is merged with a set of segments that are obtained from multi-resolution segmentation and the results are evaluated with overall accuracy and test pixels. The objectives of the study were to improve the accuracy of classification.Flowchart of the proposed algorithm presented as follows:The distinctive characteristic of synthetic aperture radar (SAR) sensors is the ability to provide a day-or-night, all-weather means of remote sensing. Recent SAR systems can produce high-resolution images of the land under the illumination of radar beams. SAR polarimetry is a technique that employs different polarization waves during transmission toward and reception from the Earth's surface and the resultant PolSAR images can be used in identification of different classes based on analyzing different polarization backscattering coefficients; by assigning pixels into different classes using a classification technique, the information contained in the SAR/PolSAR images can be interpreted.Classifier ensembles or multiple classifier systems (MCS) are methods in pattern recognition that are used for image classification; by combining different independent classifiers, MCS can improve classification accuracy in comparison with a single classifier. There are different methods for creating such an ensemble. These methods include modifying the training samples (e.g. bagging [1] and boosting [2]), manipulating the input features (the input feature space is divided into multiple subspaces [3]), and manipulating the output classes (multi-class problem is decomposed into two multiple class problems, e.g. the error correcting output code [3]). After creating an ensemble of classifiers, a decision fusion is used to combine the outputs of the classifiers. Several fusion algorithms have been developed and employed in the literature like majority voting, fuzzy integral, weighted summation, consensus, mixed neural network, and hierarchical classifier system [4], [5]. Class-based feature selection (CBFS) is a method that chooses features for each class separately to create a multiple classifier with manipulating input features. We used this method for pixel-based classification, and then fused single classifiers in two different ways described in the next section.Experimental results showed that the overall accuracy of the proposed method (90.07%) has improved compared with the single SVM classifier and pixel-based multiple SVM classifiers (83.61%).