Background and Objective: Recently, a lot of studies have been done in Anzali wetland as one of the most important wetlands of Ramsar Convention, which has a high cost due to the nature and geographical location of the wetland. Advances in technology have made it possible to evaluate natural environments more accurately, fast, and low cost with remote sensing data due to their easy accessibility, high accuracy, extensive and reproducible coverage in terms of time and space, and information extraction in a relatively short time. Because one of the most important problems in studying vegetation changes is the lack of accurate spatial information over time. Satellite imagery and remote sensing technology make it possible to achieve a better program for environmental management by relying on the information produced by it. In this study, the vegetation Classification of Anzali wetland was done by using the technique of Object base Classifications of Landsat image incorporation with fieldwork based on the wetland index of plants as well as the vegetation index (NDVI) of the study area were analyzed. Wetland vegetation Classification maps can be used to identify the amount and type of cover and planning to maintain and rehabilitate the wetland. Materials and Methods: In this study, a vegetation map based on the wetland index is considered as one of the required criteria for ecological demarcation of wetlands. First, the general vegetation areas of the wetland on the coast and around it were identified. Then, vegetation data of wetland aquatic species were collected from different wetland areas in 0. 25 m2 plots. In the land margin area, the wetland species of the wetland margin were collected with a 1 m2 plot. A total of 42 plots were collected during the spring and summer of 2019. After preparing the required images, their preprocessing including geometric, atmospheric, radiometric corrections and image enhancement were performed using ENVI. Landsat 8 Image on July 29, 1998, with a spatial resolution of 30 meters was used to classify vegetation and prepare a map of vegetation index (NDVI) and image of Sentinel-2 satellite (July 98) due to 10 m of the ground resolution was used to combine with Landsat 8 data as auxiliary data in image Classification. The combining of these two images improves the spatial resolution also preserves the spectral values of the multispectral image. The Object-based Classification was performed on the integrated Landsat 8 image using training data from field work. The Classification accuracy was evaluated for each class using experimental samples as ground control points and the Classification error matrix was extracted. Results and Discussion: First, the dominant plants and representatives of their wetland index were identified by field work. Then, the relative percentage of dominant plant cover at the sampling site was calculated according to the standard list of identified plant species, and Plants were divided into two groups of wetland and non-wetland based on the wetland index. From the Classification of plot species in 42 plots, 180 plant species were identified in 124 genera and 48 families. Also, four groups of wetland plants were: obligate wetland plants (OBL), facultative and obligate wetland plants (OBL & FACW), facultative upland, and facultative wetland plants (FACU & FACW), and facultative wetland plants (FACW). A vegetation map was prepared from a combination of terrestrial samples and Object base Classification of the 2019 Landsat satellite OLI image sensor. The accuracy of the classified maps was evaluated based on the kappa coefficient and overall accuracy. The overall accuracy is 88. 62% and the kappa coefficient is 84%. The Plant distribution was determined based on satellite image Classification: OBL plants were observed in the water zone (west and Sorkhankol wetland margin), FACW plants were observed mostly in the dry margin and mainly in the southwest of the wetland (Siahkeshim wetland) and Choukam Wildlife Sanctuary in the eastern part of the wetland, OBL & FACW group with less uniform distribution was observed in the whole area and FACU & FACW group was observed in a small part in Choukam, north, and northwest of the wetland. The percentage of vegetation density map retrieved from the NDVI index shows the distribution of dense vegetation cover in different parts of the wetland and the limitation of the water level of the wetland bed. Conclusion: The results of the satellite imagery study and their Classification according to terrestrial samples showed that the spread and dispersal of obligate wetland species (OBL) were limited to water parts of the wetlands so that the highest distribution of these plants were in the west of the Anzali wetland and Sorkhankol. The spread of facultative wetland species (FACW) was in the arid areas of the wetland, which indicates the upland areas of the wetland in Siahkeshim (southwest) and Choukam (east). The result of image Classification showed the percentage of plant group in each class: the agricultural class (with a present level of 23. 9%) and the group of facultative species (FACW) (with a present level of 23. 6% and mostly Phragmites, Alnus, and Salix species) have the top percentage of image Classification classes of Anzali Wetland. This indicates more presence of facultative species compared to obligate species of wetland (OBL) (with a present level of 10. 1%) and the level of agricultural land occupation, showed the wetland drying. The percentage of vegetation at the wetland level was assessed with the vegetation index (NDVI), most of which belongs to dense vegetation. Due to the fact that the satellite image is related to the summer season, this map shows the distribution of vegetation in different parts and the water level of the wetland bed, which has reduced the amount of water levels in the wetland. Periodic review of vegetation and its ecological changes provides useful information on changes in the water and ecological resources of the wetland to plan for its maintenance as an important ecosystem in the region.

SLAM Loop Closure Detection Information Theory Kolmogorov Complexity In this paper the problem of 3D scene and Object Classification from depth data is addressed. In contrast to high-dimensional feature-based representation, the depth data is described in a low dimensional space. In order to remedy the curse of dimensionality problem, the depth data is described by a sparse model over a learned dictionary. Exploiting the algorithmic information theory, a new definition for the Kolmogorov complexity is presented based on the Earth Mover’s Distance (EMD). Finally the Classification of 3D scenes and Objects is accomplished by means of a normalized complexity distance, where its applicability in practice is proved by some experiments on publicly available datasets. Also, the experimental results are compared to some state-of-the-art 3D Object Classification methods. Furthermore, it has been shown that the proposed method outperforms FAB-Map 2.0 in detecting loop closures, in the sense of the precision and recall.

It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted on the images, digitally. There are several parameters which must be optimized prior to use of Object oriented Classification. One of these parameters is Scale affecting the segmentation results, significantly. Scale is usually set by trial and error which is an experimental approach. One of the aims of this study is to optimize Scale parameter, automatically. In addition, after segmentation process based on a proper Scale, it is required to classify the identified segments based on the attributes which are extracted from these segments. In this stage, the selection of suitable Classification method fed by the proper attributes is critical. In this research, LiDAR data and aerial image acquired on Vaihingen, Germany, were utilized for segmenting the urban area. In order to identify suitable attributes, random forest feature selection was applied on the attributes derived from the identified segments. Machine learning methods including support vector machine, random forest, and decision tree were compared for classifying the segments based on their suitable attributes into two classes including tree canopy cover and others. The results indicated that Scale of 25 is the best one to segment this area. Also, the tree canopy cover map derived from support vector machine with quality index of 79.90 showed the best performance among different classifiers used in this study.

In this paper, a new mechanism is proposed to transform the structural modeling elements of the UML class diagram and Object-Z specifications into each other. A set of bidirectional rules is defined to transform the mentioned elements into each other. Bidirectional transformation of the UML class diagram, as one of the most useful diagrams of UML, and Object-Z specifications into each other prepares the ground for the use of the unique advantages of both formal and visual modeling methods. The feasibility of the proposed approach is evaluated using the multi-lift case study. The results of conducting the multi-lift case study show that the proposed mechanism is feasible.

In recent years, development of the machine learning algorithms has led to the creation of intelligent surveillance systems. Thanks to the machine learning, it is possible to perform intelligent surveillance by recognizing people's facial features, classifying their age and gender, and detecting Objects around instead of ordinary surveillance. In this study, a novel algorithm has been developed that classifies people's age and gender with a high accuracy rate. In addition, a novel Object recognition algorithm has been developed that detects Objects quickly and with high accuracy. In this study, age and gender Classification was made based on the facial features of people using Convolutional Neural Network (CNN) architecture. Secondly, Object detection was performed using different machine learning algorithms and the performance of the different machine learning algorithms was compared in terms of median average precision and inference time. The accuracy of the age and gender Classification algorithm was tested using the Adience dataset and the results were graphed. The experimental results show that age and gender Classification algorithms successfully classify people's age and gender. Then, the performances of Object detection algorithms were tested using the COCO dataset and the results were presented in graphics. The experimental results stress that machine learning algorithms can successfully detect Objects.

Damage caused by the sand dunes movement is one of the most important environmental and socio-economic issues in desert region. erosion and wind processes study began with the work of Bagnold (1954). After significant advances in laboratory and physical approaches to the elements and forces involved in wind erosion and at the contemporary with the development of remote sensing tools and data and changes in methods and algorithms for interpreting aerial photographs and satellite images, the rapid emergence of planetary geomorphology and the search for analogies and similarities on other planets. Rapid developments in the geomorphology of wind processes took place. Using Landsat, ASTER and Quick bird images and LIDAR data, many studies have been done to classify sand dunes. After the launch of two ERS remote sensing radar satellites in 1991 and 1995, the value of CCD Was considered. But in Iran, most of the studies conducted in the desert region, such as the Damghan playa, have studied the changes in long-term periods, which are mainly of sediment origin and classified sand dunes using multispectral satellite data. The aim of this study was to use Sentinel-1 IW SAR time series data in arid regions to detect surface changes in the short term due to wind morph dynamic activity and on the other hand to evaluate the effectiveness of using both radar and optical data and Object-oriented Classification model in the events and morphological changes detection of sand surfaces and forms. the results obtained from the processing of remote sensing data and Classification and achieving the dimensions of sand dune mobility with the results of wind data analysis will be evaluated and verified. Materials and methodsDamghan plain located in Damghan plain with longitude 54 10 to 54 40 east and latitude 36 36 to 36 10 north has a hot and dry desert climate and an average rainfall of 100 mm per year, which due to the desert nature, is prone to wind morph dynamic performance. Therefore, in the present study, we aimed to evaluate the mobility of sand dunes as part of the natural hazards active in the region. The research method is library, remote sensing and surveying. Data analysis is based on two main concepts,segmentation and Classification. Initially, based on geological and topography maps and field survey, geomorphology maps were created. Then based on the prepared and adapted maps and field surveillance, sandy forms were limited. Then, in order to determine the working units, using the CCD technique with Sentinel-1 radar images, the active and inactive parts of the sand forms were detected. Two radar interfrograme (Master and slave) related to the two dates of 14/05/2017 and 22/03/2018 were used to extract the CCD (based on phase difference). Finally, with the identification of work units, automatic detection and extraction of sand dunes was targeted, and for this purpose, the bottom-up hierarchical Object-oriented method and top-down Classification using the growing region technique was used. Also, by extracting sand dunes using Object-oriented Classification, the values and direction of moving the dune were extracted using Guy, 1995 optimized model and the corresponding sand rose were drawn. Wind rose analysis and drawing related to wind statistics of Damghan synoptic station (the closest meteorological station to the study area) in the statistical period of 1384-96 was also performed with the aim of verifying the findings of the previous step. Result and discussionA: Extract sandy formsImage enhancement is the first step in preparing an image for the extraction of image elements (including sand dunes). Due to the importance of the dune slip face, in the process of identifying the displacement and sand dunes movement, and its lower compaction coefficient than other parts of a sand dune, in order to detection This enhance method, by using the most abundant discontinuity search, distinguishes brighter borders that forming sand dune steep slope from other parts of dune and around environments. The output of this filter is an image in which the sand dunes slip face, with different radiometric intense, is marked from the surrounding sand surfaces. B: Detecting and extracting sand dunesIn order to evaluate the displacement amount and direction, the Object-oriented Classification paradigm was used to automatically detect the edges as dune front. Instead of just evaluating pixels, the spatial pattern of Objects and forms is also considered. Therefore, the initial segmentation was performed using a scale factor "100" that determined the maximum heterogeneity in the diagnosis of the forms. in addition to using radiometric values, classes can be formed based on geometry and related elements. The rules used are Brightness and Compactness. First, by analyzing the values of average brightness with a threshold of 165, the overall sand dune pattern as the first layer was created. Then, using the Compactness rule, the pixels that were recognized as the dune slip face by the spectral feature in the previous step were eliminated from the classes. C: calculation the sand dunes amount and direction displacement. Sand dunes displacement calculated by considering the end edge, as the progressive edge at successive times and measuring the distance between two consecutive lines in two consecutive years. To evaluate the dunes movement direction, the axis of symmetry of each hill was selected as the main axis and the initial and final point of this line on the downwind front of sand dunes in both the first and last years were considered. The azimuth line or the direction of movement relative to the north was drawn and this angle was calculated and its sand rose with an angle of 135 degrees was drawnConclusion This study, suggested a new approach to detect sand dynamics using radar InSAR techniques and Object-oriented Classification using high resolution optical images. The results of InSAR processing, and CCD technique, was able to recognize active and inactive sand dunes dynamic, and display them in continuous numerical values (fully active to fully stabilized hills). The application of OBIA on Bird’, s eye and Geo eye images (2003-2016) results, indicates that the 22. 4 m movement of hills is mainly in the southwest direction in a period of 13 years and 1. 7 m for each year. The result of comparing wind rose (wind data analysis) and sand rose (sand movement data analysis) shows a significant relationship between 80% of northwest-southeast wind frequency in relation to 135 °,azimuth for 75% of sand dunes movement and 15% of north-south wind frequency in relation to 180°,azimuths of 25% of sand dunes movement.

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%).

Surkh Dum-i-luri is located in 10 kilometers of southeast of Koohdasht city in Luristan province. In 1938, this area was explored and excavated through the Holmes investigation commission supervised by Schmidt. According to the archaeological discoveries, Surkh Dum has three stratigraphic layers. The main period of settlement in this site (layer 2) consists of a great architectural collection named temple. During the excavation of the surface floor of the architectural places, they discovered 1804 Objects under the floor and in the walls. Regarding to the number of discovered Objects and their different variety, generally, there is an appropriate opportunity to study the relation between the discovered building and the mentioned Objects. Despite of their large number, these Objects have not been classified and introduced yet. This study has been done by descriptive attitudes and relying on library data and archaeological survey. According to the topological results, Surkh Dum-i-luri Objects consist of ornaments, seals, pins, tools, vessels, figures, weapons and miscellaneous Objects. Ornaments and bronze are the most abundant elements, in terms of number and used material to make, respectively. Furthermore, study on the statistical population and pattern of these distribution show that these Objects are votive and this place has an important role as a temple. Excavation of Surkh Dum-i-luri in Luristan by Schmidt shows the developments.

Forest Classification on the basis of satellite images is a promising technique both for primary map production and for map updating and forest monitoring. For accurate forest Classification into three classes, using mapping by canopy cover density “high spatial resolution satellite images have to be used in order to obtain the required spatial detail” [Schneider, 1999]. At the same time, the spectral information necessary for identifying certain class types can most economically be derived from multi-spectral images of medium spatial resolution. Fusion techniques have to be used to combine information from both sources. In this paper, a method was developed for Object-level fusion of IRS-1C/1D pan images (5.8 m pixel size) and LANDSAT TM multispectral images (30 m pixel size) and subsequent Classification to produce a canopy cover Classification of the northern forests of Iran. The study area is located in Sari and its forest regions in 60,000Hec. (Figure 1) The individual processing steps included segmentation of a multi-band image consisting of both the high-spatial-resolution pan image band and medium-spatial-resolution multispectral bands, with proper weighting of the individual bands in the segmentation procedure in order to obtain both fine detail from the pan image and coarser boundary delineations which show up only in multispectral images. For Classification, fuzzy logic membership functions were used. Verification of the Classification was carried out and checked with error matrix and kappa calculation on a selected transect from a newly classified map. The results showed that employing Object-based fusion procedure using medium- and high-resolution data was an appropriate method that improved Classification. Comparing the hard work of creating a new topographic map, a pixel-based fusion procedure was demonstrated to be an acceptable method to create a satellite image map (Satmap) for visual monitoring activities and programs. The overall accuracy of the map produced was calculated as a topo-map of the region.

Hyper Spectral Images (HSI) collect a lot of information in hundreds of narrow spectral bands. This type of image has been more useful for a wide range of applications in ground surface identification. Here, there are some processes to achieve that proper information. So, finding a way to gain the best accuracy for collecting data has become an interesting field for scientists. As a result, in this paper, we introduced Object-based Feature Extraction algorithms (FE) to find out such useful information. The proposed algorithm has four fundamental phases. In the first stage, we use an unsupervised FE such as the PCA algorithm to extract the most significant features of the image. Then, the Gabor filter would add to obtain the local features. In the third step, we use the K-means algorithm to make a segmentation map of the image. Finally, in the last stage, by considering the coordination between pixels of each region and the effects of local relations among neighbor pixels relating to the same Object in the image by an appropriate transformation, a function introduced. As a consequence of all these stages, some important and efficient features of the proposed data would extract.