فهرست مطالب علیرضا عرب سعیدی

Providing information layers including the most important means of facilitating land use in urban areas are in the process of crisis management. Urban management based on their knowledge and information layers of the urban side, preclude a broader crisis. Remote sensing technology and fast as science and technology plays an important role in the production of information. Urban effects and a wide detection of satellite images have useful applications in various fields, including automating the process of identifying and extracting data from images and update database information system locations. The purpose of this research is to introduce and implement automated way to extract information congested urban land cover in the region, based on object-based methods and analysis in the context of satellite images with high spatial resolution. After the proposed procedure hierarchical segmentation process, to produce the texture descriptors for each of the events will be discussed. The method proposed in this study was 86% overall accuracy and kappa coefficient 0.83 satellite image was obtained for the detection of complications city.

One of the applied studies in the field of object-based analyses is the extraction of urban features. Buildings are one of the most important urban features in large scale maps, so the process of identifying and extracting these features is especially important in the location-based processes of the passive defense. Due to the variety of spectral and geometric properties of these types of features, their identification in different study areas is associated with some problems. In this study, using an object-based analysis and extracted features from laser data and aerial photos to identify buildings with a sloping roof are discussed. In the first step, after the segmentation process, the separation of high and non-complicated features is done using the slope and gradient direction. In the next step, by extracting the geometric and conceptual features, the separation of trees and buildings from each other is done. In the final step, the reconstruction of the lost buildings is performed by morphological operators. The combination of two categories of input data at the decision level leads to benefits from the characteristics of both data, and each of them can cover other problems and shortcomings. In the proposed method, a rule-based strategy is based on the production of geometric and conceptual features and the use of a multi-stage approach. Finally, the overall accuracy of the building category identification is 87%, and the kappa coefficient is 81%. The results demonstrate the high capability of object-based methods to identify urban features, such as buildings, by combining Lidar data and aerial photos despite the diversity of urban environments.

Given the diversity of urban problems and the sensitivity of each of the specified spectral range of the electromagnetic spectrum, various imaging sensors exhibit different behaviors in relation to some of the toll record. The color distortion and respond to abnormal sensor spectral range identified as one of the most important challenges the correct diagnosis in Remote sensing, effects, and advocacy. Data integration in order to take advantage of spectral data recorded by the various sensors in better detection vegetation is also significant benefits. In this study, the results of two Landsat 8 and WorldView-2 images merge in order to better identify areas of vegetation in the urban area, using statistical transfer PCA is used. Among the benefits that this integration as well as integration and coherence spectral data cited two sensors. Using Landsat 8-band SWIR image, increase transparency and thus improve detection and associated vegetation. In this paper, after the merger of two images taken from an urban area in Tehran at the level of spectral characteristics, process knowledge base to identify and classify areas of vegetation was carried out which resulted in the mining areas of vegetation with an accuracy of 81.3% was achieved.