Mines with their displacements cause damage to infrastructure and land, so it is important to control the displacement caused by mines. Also, the amount of minerals extracted from mines has always been of interest to managers. On the other hand, radar interferometry, given the production of repetitive and full images and the acceptable accuracy of this method, in examining be suggested for the study of displacements caused by open-pit mines against methods such as Leveling and Global Positioning System that are generally expensive and timeconsuming. The surveyor engineers are responsible for calculating the displacement and volume of excavation in open-pit mines using the Leveling and Global Positioning System. The purpose of this study was to calculate the displacement caused by excavation in open-pit mines and calculating the volume of excavation. Because of the large-scale displacement in open-pit mines, the maximum displacement that can be extracted by radar interferometry technology is controlled by two factors: a) the wavelength of the radar images and b) the dimensions of the radar images, by increasing the wavelength of the images or reducing the pixel dimensions of the radar images, the maximum displacement gradient generated by the interferometric radar can be achieved.
On the other hand, rapid exploitation in open-pit mines creates decorrelation between the SAR images; To eliminate decorrelation images, selecting images with small spatial and time baselines can solve this problem. In this study, the Gol Gohar mine in Sirjan was studied using Envisat satellite radar images. Radar image processing was also performed using StaMPS software. In this study, the displacement rate is increased by changing the amount of multi looking factor images from 20 for azimuth and 4 for range to 5 for azimuth and 1 for range (reducing image pixel dimensions). After reducing the pixel dimensions of the image the maximum displacement obtained for a point in the mine changed from -51 mm to -75 mm, and also extracted volume changed from 61,000 cubic meters to 67,000 cubic meters after. So, by reducing the pixel dimensions of the radar images, the amount of displacement gradient is more detectable, and the value volume of excavation from mine will be more realistic.

The detection of buildings using SAR radar images is of great interest due to its widespread use. But the complexity of SAR images poses many challenges to object recognition. One of the factors affecting the brightness of SAR images is the geometry of the vision, which causes the deformation of the building and in some cases even the omission of the buildings in radar images. The geometry of view includes incident angle, look angle, squint angle and direction of imaging, which is less discussed in research sources. In this paper, we intend to extract the buildings that are affected by changing the geometric parameter and analyze and prove the reason for its change. In other words, by detecting images with different incident angles, the orientation of the buildings affected by this change is identified. For this purpose, the time series images of Sentinel 1 satellite related to Baghistan region of Tehran are used. Using the CMV2 change detection method, the results show that changing the angle of impact by 10 degrees, leads to the removal of the workshop or factory sheds lying in the direction of 20 degrees to the horizon from the radar image.

In recent years, Road detection and road extraction from satellite images with the advancement and development of deep learning algorithms in the field of semantic segmentation has received more and more attention of researchers. In this regard, most of the studies have been done in the field of Road detection and road extraction using optical images and in these studies, few studies have been performed using radar images worldwide. Therefore, the aim of this study was to use a deeply Refined Deep Residual Convolutional Neural Network (RDRCNN) to evaluate and compare the accuracy of road extraction from Sentinel 1 radar images in Tehran and Shiraz metropolitan areas in equal conditions in terms of number of educational samples, validation and architecture. It is the same. In this study, to extract the road using DNN, the VV-VH color combination of Sentinel 1 radar images from 8 different cities (Tehran, Mashhad, Isfahan, Shiraz, Tabriz, Urmia, Baghdad and Beijing) was used. Finally, the RDRCNN model with a residual connected unit (RCU) and a dilated perception unit (DPU) was used for road training and extraction. The research findings indicate that the RDRCNN model has performed almost the same in the process of identifying and extracting roads in the two cities of Tehran and Shiraz, and in general, the above model has performed slightly better in the city of Shiraz. In terms of accuracy evaluation metrics, for Tehran images, the criteria were Recall 57.66%, accuracy 51.29%, F1 score 54.43% and overall accuracy 92.78%, and for Shiraz images Recall criteria 60.77%, accuracy 54.71%, F1 score 57.40% and overall accuracy of 95.63% were obtained. The findings of this study show the low accuracy of road training and extraction from Sentinel 1 radar images for two metropolitan areas of Iran. In general, by comparing the results of this study with previous studies, it can be seen that one of the most important reasons for the low accuracy of the results is the low width of roads in Iranian cities; However, due to the lack of necessary studies in the field of road extraction with Sentinel 1 radar images, it is not possible to comment definitively on the results and it is suggested that more studies be done in this field.

In command and control, it is necessary to provide clean data to the system for better decision-making in the battle scene. In this regard, artificial aperture radar is an imaging radar with high resolution, which needs to improve the quality and classification of these images to recognize the details of the scene. The presence of speckle noise is the most important factor in image quality degradation, and it is necessary to reduce the effect of speckle noise in the pre-processing stage. Also, one of the important methods in interpreting SAR radar images is the classification of images, which is very useful in examining the changes in ground targets, because the observation and monitoring of targets in command and control is considered a good tool. Determining a classification method with appropriate accuracy for SAR radar images with high spatial resolution is a goal in this article, as the old algorithms in this field such as Kmeans, Perceptron, SVM, and MLP have poor detection power and inappropriate speed. As a result, in this article, the aim is to present a powerful algorithm for optimizing the classification of SAR radar ground targets with the help of the proposed method of combining convolutional network and genetic algorithm, which can be obtained through airplanes, spacecraft or satellites to observe ground targets. has been In this article, in the pre-processing stage, after reducing the effect of speckle noise on SAR radar images with the help of the Lee filter, the optimization of the classification of SAR radar ground targets with the proposed method has been obtained, and acceptable results have been obtained. The proposed network has performed 99.33% better than other methods in terms of classification accuracy of denoised MSTAR images.