Land use/cover maps are the basic inputs for most of the environmental simulation models; hence, the accuracy of the maps derived from the classification of the satellite images reduces the uncertainty in modeling. The aim of this study was to assess the accuracy of the maps produced by machine learning based on classification methods (Random Forest and Support Vector Machine) and to compare them with a common classification method (Maximum Likelihood). For this purpose, the image of the OLI sensor of Landsat 8 for the study area (Sattarkhan Dam’ s basin in the Eastern Azerbaijan) was used after the initial corrections. Five land uses including urban, irrigated and rain-fed agriculture, range and water body were considered. For conducting the supervised classification, ground truth data were used in two sets of educational (70% of the total) and test (30%) data. Accuracy indexes were used and the McNemar test was employed to show the significant statistical difference between the performances of the methods. The results indicates that the overall accuracy of Support Vector Machine, Random Forest, and Maximum Likelihood methods was 96. 6, 90. 8, and 90. 8 %, respectively; also the Kappa coefficient for these methods was 0. 93, 0. 81 and 0. 83, respectively. The existence of a significant statistical difference at the 95% confidence between the performances of the Support Vector Machine algorithm and the other two algorithms was confirmed by the McNemar test.

Introduction: In general, satellite data classification with digital interpretation method is separation of similar spectral sets and classification of images into groups or classes placed in each class, spectrum or a value (not being separable statistically). In other words, when a classification is carried out on data sets or images, in fact spectral classification is conducted and in the new image, every class or category is indicator of a specific phenomenon with a unit spectral value. The base for data classification is comparison of spectral value of picture pixels with the samples introduced by its interpreter or with initial classes and categories, constituted automatically in the time of digital interpretation. Hence, the pixels, values of which are not statistically significant, are placed in the same spectral group or class. So, preparation of land use maps by digital classification of remote data sensing depends on the methods used in classification, land coverage condition and environmental condition of the area. The aim of the present research study is to compare pixel-based classification algorithm (maximum likelihood) with object-based method (support vector machine) to prepare land use maps with the aid of SPOT5 satellite HDR image sensor and Landsat 8 satellite OLI image sensor in Ahar Chai basin from Ahar area to Varzegan. Methods and materials: In this research study a SPOT5 satellite HDR image sensor dated back to August 23, 2006 with four spectral bands and separation power of 10 m with path and passage 138-272, 138-273, 139-272 and 139-273 and Landsat 8 satellite OLI image sensor dated back to 2017/07/21 with route and passage 168-33, isolation power of 30m, increased to 15m through combination with band 8 were used to collected the necessary data. Topography map at scale 1/50000 and geological maps of 1/100000, land control data harvested by GPS, Garmin model and ArcGIS 10. 4. 1 and ENVI 5. 3 software were used in this study. Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. Maximum likelihood Classifiers (MLC) which use pixel-based and object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM). Therefore, the aim of the present research is to compare maximum likelihood in pixel-based processing and support vector machine algorithm in object-based processing in order to evaluate their performance in classification of LANDSAT 8 and SPOT 5 satellite images. The necessary pre-processing including geometrical corrections and atmosphere corrections were conducted on the image. Discussion and Conclusion: Findings of the study indicated that both support vector machine algorithm in object-based classification and maximum likelihood in pixel-based classification enjoy higher and appropriate accuracy in classification and exploration of land use maps for Ahar-Varzegan area. However, considering the results obtained from the two algorithms used in this research, it can be concluded that support vector machine algorithm in object-based classification of satellite images provide the conditions for higher accuracy compared to maximum likelihood in pixel-based method. One of the main reasons of achieving higher accuracy in support vector machine algorithm classification is that in this algorithm, in addition to spectral data the data related to contexture, shape, position and content are also used in classification process, hence classification accuracy is increased significantly. After doing classification procedure using both algorithms of pixel and object-based method we proceeded on evaluation of the results reliability in them Conclusion: The results showed that, Findings obtained from both classification algorithms indicated higher accuracy of object-based classification with total accuracy of 94. 99 % and Kappa coefficient for exploration of land use of Ahar-Varzegan area, this is while pixel-based classification with total accuracy of 89. 39% and Kappa coefficient of 0. 89% indicated acceptable level of classification. So that Feizizadeh et. al (2009) in exploration of East Azerbaijan land use with the aid of the two pixel-based and object-based algorithms concluded that object-based classification with total accuracy of 95. 10 % has higher efficiency in exploration of the province land use compared to pixel-based classification with total accuracy of 88. 37%.

The Karkhe River is one of the most important rivers in Iran and is the third largest river in terms of the discharge volume. In its wild state. the river usually left many damage its wake. In order to reduce these detrimental out comes, the Karkhe Dam was built, which is one of the most important and also the largest dams in Iran and the Middle East. This dam has instigated an economic reforminits basin, such as changes in the land-use, amount of water, vegetation and the urban areas. Some of the major changes occurred ofter the dam construction have been evaluated: The Using Landsat satellite images spanning between 1352 and 1392, maximum likelihood classification identifying 7 classes was conducted on the pre-processed images. The results showed the barren soil decrease of 0.2 percent, the residential area, vegetation and water supply have increased by 2.36, 1.4 and 2.5 percent, respectively. In spite of the logical trend of these results, the accuracy assessment was as an added measure to confirmed the previous results. The evaluation showed a high accuracy almost in all of the classification results. The overall accuracy and the Kappa coefficient estimated from the accuracy assessment are higher than 90% and 0.9, respectively, while the user and producer accuracies are more than 80%. This demonstrates the high performance of the maximum likelihood classification.

Impenetrable surfaces are an important factor in monitoring urban development and environmental quality. For this reason, recognizing these levels will be a precondition for achieving sustainable urban development. Although there are various methods for detecting and studying these levels, accurate and cost-effective determination of these levels is still an important challenge for urban researchers. Due to the increasing availability of remote sensing data, in this study using the GeoEye 2009 image, the accuracy of the three most common classification methods, artificial neural network and support vector machine for determining impermeable surfaces in a part of Bandar Abbas city was compared. For this purpose, after performing the necessary preprocessing operations on the image, using the aforementioned algorithms, five classes of street and building (as impermeable surfaces), water body, vegetation and wasteland (as impermeable urban surfaces) for each Three methods were extracted. To evaluate the results, methods of overall accuracy, kappa coefficient, user and producer accuracy were used. The results showed that the support vector machine with 94. 7% overall accuracy and kappa coefficient 0. 93, compared to artificial neural network method (93. 1% overall accuracy and kappa coefficient 0. 90) and the most similarity method (with accuracy). The overall accuracy was 92. 2% and the kappa coefficient was 0. 89). Although the present study showed that the support vector machine method was more accurate, nevertheless, the accuracy of the most similar and artificial neural network methods were accurate and acceptable in determining impermeable surfaces and processing of high spatial resolution images with these methods, Can detect impermeable surfaces.

Built in the early 14 th century, the Jame Mosque of Yazd was developed and extended through the ages. Its famous entrance portal was built in the 16 th century to act not only as its prominent architectural feature, but also as a city landmark. Its bold elongated proportions which are not without mystical meaning have an everlasting impact on the visitor. The Pound bury tower, Dorchester, England was designed by the well- known architect, Leon Krier towards the end of 1980s. The monumental structure bears outstanding resemblance to the portal of the Jame Mosque, as though the tower design was almost completely inspired by the mosque portal. The article analyzes the similarities of the two structures in terms of their geometry, proportions and formal features such as arches, buttresses, and openings.