جستجوی مقالات مرتبط با کلیدواژه « تصحیح هندسی » در نشریات گروه « جغرافیا »

Today, the integration of agricultural lands is one of the most basic measures in the development and progress of the agricultural industry. In order to integrate agricultural lands, we need to implement the agricultural property cadastre plan. The cadastre includes a map and descriptive information of each property. Cadastral maps are maps that we must prepare or have a database to prepare and update. Databases on the one hand contain descriptive and spatial information of the reference land and on the other hand include techniques of collecting, updating, processing and distributing data. To manage databases, you must use a variety of spatial information systems. The cadastre provides a single and correct identity for each piece. Theoretical and practical advances in various technologies such as remote sensing satellite imagery and GIS in data collection management can improve quality, cost, efficiency and effectiveness. An appropriate technology should be selected for mapping and maintaining geometric cadastral information along with strategic goals. Advantages of using satellite images, time consuming and high workload of using aerial images in the production and updating of topographic maps, as well as issues related to flying over the border areas of the country, the need to study and study more on satellite images more than It enlightens us. In this research, first, a regular review of IKONOS satellite image potential measurement was performed in order to extract the information layers of cadastral maps (large-scale) and ground information systems. That is, to what extent does a satellite image have the capacity to extract information layers in terms of geometric and content accuracy? After that, we entered the extraction of agricultural boundaries from the satellite image, which is the most important complication in the agricultural real estate cadastre. Extraction of agricultural border is based on image edge extraction operations, visual identification and image classification. In evaluating the geometric accuracy of IKONOS image in theory, the result was that the geometric accuracy of this image is optimal for 1: 5000 maps. The evaluation of the geometric accuracy of the IKONOS image using Grade 3 two-dimensional polynomial equations is related to the selection of 95 control points and 5 check points with the desired value of RMSE = 0.56M for 1: 2000 maps and the best combination using rational equations with 95 Control point and 5 checkpoints in the use of 13 sentences with RMSE accuracy = 0.4M optimal 1: 1000 maps and the best combination using SPLine equations with 80 control points and 20 checkpoints RMSE accuracy = 2.6M optimal 1 maps : 5000. In terms of content, IKONOS image is suitable for viewing, recognizing and extracting the information layer of the border of agricultural properties in different areas of the image for preparing large-scale maps or agricultural real estate cadastre.

The geometric correction of high resolution satellite images (HRSIs) using proper mathematical models is an important step in 3D spatial information extraction process. To do it, some control information such as points, lines or areas should be extracted at first. Then, the correspondence features are determined and used directly to estimate the unknown parameters of a mathematical model. Since, the accuracy of geometric correction of satellite images directly influence the accuracy of georeferencing procedure, in this paper, the issue of solving the unknown parameters of a rational function model (RFM) based on linear features as well as their combination with point features as control information is studied. In addition, the impact of each control information to reduce or omit the systematic errors are also studied. The obtained results show that the accuracy of rational models based on control lines is very low and about 3 pixels. Additionally, the residual vector diagrams demonstrate the existence of enormous amounts of systematic errors. The reason in addition to the accuracy and distribution of control lines could be due to the characteristics of linear features used as control lines. In other hand, the integration of control lines and points to estimate the unknown parameters of the model increases the accuracy as well as reduces the systematic errors significantly. Therefore, the results show the high potential of the integration of point and linear features to improve the accuracy as well as reducing the systematic errors.

This paper proposes a new approach for geometrical modeling of satellite imagery which uses 2D-polynomials for 3D point determination from satellite stereo images. In this model, 2D polynomials are considered as additional parameters in colinearity equation, instead of considering as models for relating between the ground and space images. Orbital parameters model are used as fundamental colinearity equations in this modeling. Essential parameters in the orbital parameters model are determined from satellite ephemeris data and they are considered as fixed parameters in the modeling. In this model, polynomial coefficients are the only unknown parameters which are determined from GCPs in a linear equations set. The major advantages of this model are: Decreasing the performance complexities in using orbital parameters model, ease of implementation, applicability on raw geometrically corrected images, and possibility of using maximum capacity of satellite auxiliary data, and linearity of equations in space intersection procedure. Implementation of this model on different datasets shows high potentiality of the mentioned approach for 3D point determinations from satellite stereo images.