In the current state of colonization of near Earth space by SATELLITEs, there is an increasing need to know exactly the real status of occupation of this space. Thus, orbital parameters for all objects travelling in this space must be known with a high degree of accuracy, and this knowledge must be periodically updated, because this situation is always changing. Atmospheric drag, solar wind, moon and planetary gravitational perturbations, Earth oblateness, etc. are all sources of interference that generate orbital perturbations beyond what the best orbital model can predict. The solution is to periodically observe all the SATELLITEs, particularly the debris (because active SATELLITEs themselves contribute to maintain the knowledge of their orbital parameters), determine with precision their positions and update their known orbital parameters. There is a real need for sky surveillance in order to monitor either the SATELLITEs or the non-functional space objects for different purposes, such as to correct the SATELLITEs deviations from their trajectories, to detect uncataloged space debris objects and to avoid possible collisions. In order to define the location of the SATELLITE in the sky and then to update its orbital parameters, an optical SATELLITE tracking system can be designed which acquires sequences astronomical IMAGES from the sky. Such system is composed of many sensors like a telescope, a CCD camera, a GPS receiver, etc. Also, some reference data such as the star catalogues and the Two Lines Element (TLE) database are used. The telescope is used to search the sky and point to the SATELLITE, precisely. The CCD camera acquires some sequences IMAGES in a current time provided by GPS. The star catalogues are employed to calibrate the image plane to the celestial coordinate systems. The TLE database contains the out-dated orbital parameters to estimate the SATELLITE position. For this purpose an algorithms and software that can automatically detect and report the presence of SATELLITE streaks in the acquired IMAGES are needed. The algorithms presented in this document were developed for this purpose. The image processing technique presented in this document is a collection of algorithms used to detect and classify everything that can be observed in the image, such as stars, SATELLITE streaks and image artefacts. First due to the use of digital imagery, the quality of digital IMAGES is critical and affects the final product. Different noises in imaging phase could degrade the quality of image, for this purpose the non-linear diffusion filter has been used. This technique, is based on the use of partial differential equations, the idea behind the use of the diffusion equation in image processing arose from the use of the Gaussian filter in multi-scale image analysis. Second for the removal of the image background the stars have been detected using SIFT method. In this method the star's centers are extracted with sub-pixel precision, then they have been subtracted from image in an iteration producer. Third the clustering method has been applied for SATELLITE streak detection. In this way the Density-based spatial clustering of applications with noise (DBSCAN) which is a density-based clustering algorithm has been used, finally MSAC algorithm has been implemented for streak model extraction.

SATELLITE image segmentation, as a main step of remotely-sensed image processing, is often accomplished by clustering when ground truth is not available to provide samples to train a supervised classifier. To solve this problem, here we propose a new purposes approach for fuzzy segmentation error reduction fuzzy logic-based algorithms as well as structural information is utilized in our proposed multi-resolution Fuzzy C-Mean (FCM) clustering algorithm. The results show that the multi resolution based FCM can improve the result of the standard FCM for an unsupervised classification approach.

One of the most important applications of SATELLITE IMAGES is the use of it to determine and estimate the surface temperature of the earth. To investigate this, the Landsat 8 series was used to calculate and estimate the air temperature in the city of Isfahan and surrounding area. The surface of the earth's surface temperature (LST) was calculated using the Subrino window algorithm. The results showed that the use of this method on SATELLITE IMAGES of Isfahan region, which covers a variety of urban areas, green spaces, and major industrial areas, shows the ability of this model and the proportion of these IMAGES for this purpose. The comparison of a temperature measured on the image and the temperature calculated in two of Ozone-metric and Airport stations in Isfahan shows a range of changes of about 4. 5 degrees Celsius. However, the calculated temperature in four days of the year is more than the measured temperature at a depth of 5 cm in the soil at the site of the two Ozone-metric and Isfahan Airport stations and this value is at a maximum of 4. 25 degrees Celsius above the station's temperature. The correlation relations between the LST surface temperature map and the normalized vegetation difference index (NDVI) at 9100 pixels of the area code with a correlation coefficient of 0. 445 are confirmed at a confidence level of 99% which confirmed the validity of the method used to estimate the surface temperature. .

A considerable portion of potable water in Iran is supplied by natural glaciers, and then the study and protection of these resources are a necessity. This investigation includes the assessment of glacier parameters such as maximum and minimum altitude, area and perimeter, position of snow line and etc. Since direct measurement of these parameters in the field is time consuming and expensive, therefore, some techniques such as remote sensing seem to be more useful and plausible.  In this regards, one can deploy different algorithms for detection of the glacier region as well as calculation of relevant important parameters.To achieve this goal, the resolution of SATELLITE IMAGES in spatial, spectral and radiometric aspects should be studied and assessed.  In this research, SATELLITE IMAGES with different resolutions have been used to study the Alam Chal Glacier. Using different SATELLITE IMAGES, the glacier parameters have been identified and studied and the most appropriate IMAGES which can provide the necessary precision for this task were identified.Also the potentiality of DEM (Digital Elevation Model) in combination with SATELLITE IMAGES in order to obtain the glacier geometric elements such as the topography of cirque, maximum and minimum height, the topography of the district and watershed, has been investigated.

Matching remote sensing IMAGES is a challenging issue in computer vision applications. Due to the very large dimensions, local destructions, radiometric distortions, and geometric changes in the input IMAGES, the existing matching algorithms such as Scale Invariant Feature Transform (SIFT) produce a large number of false matches. Moreover, due to the high dimensional IMAGES a big number of keypoints are extracted in large-scale SATELLITE IMAGES. A very large number of keypoints increases the computational, memory and time complexity in the stages of feature description and matching the keypoints. In this paper, the geometric relationships between the key points extracted from the input IMAGES, are used to improve the detection process of false corresponding points and also to increase the speed of the SIFT algorithm. The proposed false correspondence removal algorithm uses the histogram of the scale difference values and the two image rotation angle. In the following, two new algorithms which are based on the hierarchical strategy are proposed to increase the speed of the SIFT algorithm. The first proposed algorithm is based on finding the optimal octaves in the scale space of the SIFT algorithm and selecting their compared keypoints. In the second method, the parameters of the affine transformation which are between the two IMAGES are calculated by performing an initial matching, and then this transformation is used to reduce the search space in the final matching stage of the keypoints. Finally, to check the performance and accuracy of each of the proposed methods, a variety of simulated and real IMAGES have been used. Moreover, for the final evaluation of the proposed algorithms, the obtained results are compared with SIFT, SR-SIFT and SIFT-GSI methods. The experimental results confirm the accuracy, stability and high speed of the proposed methods in matching SATELLITE IMAGES.

Considering ongoing developments in Photogrammetry and Remote Sensing and attending to their applications such as digital true orthophoto generation from high resolution IMAGES, have already proven the urgency and necessity of approaches for generating DEM as input of these applications. In this research we describe a novel method for automatic DEM generation. We describe “Semi-Global Matching” algorithm then implement and test it for generating an initial disparity image from high resolution stereo SATELLITE IMAGES. Furthermore, post-processing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are needed, to generate an accurate disparity map. With generating a clear disparity map we can generate 3d point clouds. Then these point clouds are used for generating a precise DEM for other applications.

Water is one of the most important resources needed by human society and the first and most important factor for the production of agricultural products, more than 90% of this vital liquid is consumed in this sector. One of the most important factors that affect the performance of a water conveyance and distribution network is the water distribution and delivery program. In order to obtain turnouts’ discharges, the water requirement of the eastern Aghili area was estimated using the Global Land Data Assimilation System (GLDAS) and controlled using the results of the NETWAT model. For this purpose, three-hour evapotranspiration was estimated with GLDAS, and the six-hour discharges of turnouts were calculated according to the cultivated area of each turnout and irrigation efficiency. The hydraulics of the eastern Aghili canal were simulated using the above-mentioned data for six hours. The results showed the appropriate accuracy of GLDAS so that at a maximum of 12.7%, GLDAS underestimated the evapotranspiration values compared to NETWAT. The minimum values of efficiency and adequacy indicators of 0.95 and 0.94, respectively, were obtained, which are in the "good" performance class.