Mojtaba Behzad Fallahpour; Hamid Dehghani; Ali Jabbar Rashidi; Abbas Sheikhi
Abstract
Abstract
Effective factors in SAR images can be divided into five general categories of radar, radar-carrying platform, channel, imaging domain and raw data processing section. In each of these factors, various physical, structural, hardware and software parameters are influential, in such a way that ...
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Abstract
Effective factors in SAR images can be divided into five general categories of radar, radar-carrying platform, channel, imaging domain and raw data processing section. In each of these factors, various physical, structural, hardware and software parameters are influential, in such a way that one can see the role of each of them in the final formed image.
Modeling, Analyzing and, in general, knowing the effect of each of these parameters, will provide a better understanding of how SAR imaging systems operate, and from this point of view, it will not only be an important step in designing and manufacturing these types of systems, but also it will provide the possibility of interpreting and analyzing these types of images. For this purpose, in the present paper, the effect of the angle of incidence and the shape of the targets which are parts of the radar and the imaging domain parameters, are simulated in SAR images. The shapes used in this simulation are cylinders, cones and cubes, which represent buildings, silos, tree trunks, etc., in the real world, so they are very abundant in SAR images. Also, for more comprehensive results, different angles of incidence of 30, 40, 45, 50 and 60 degrees have been selected for simulation. With this simulation and analysis of the results, the behavioral pattern of the above geometric shapes is extracted at different angles of incidence from the perspective of SAR imaging systems. Thus, an important step in identifying and recognizing various shapes, which is one of the most important issues in the interpretation of SAR images will be taken.
Hamid Reza Ranjbar; Ali Reza Azmoude Ardalan; Hamid Dehghani; Mohamad Reza Serajeyan; Ali Alidousti
Abstract
Earthquake is one of the most catastrophic natural disasters to affect mankind. One of the critical problems after an earthquake is building damage assessment. The area, amount, rate, and type of the damage are essential information for rescue, humanitarian and reconstruction operations in the disaster ...
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Earthquake is one of the most catastrophic natural disasters to affect mankind. One of the critical problems after an earthquake is building damage assessment. The area, amount, rate, and type of the damage are essential information for rescue, humanitarian and reconstruction operations in the disaster area. On the other hand, to deal with the situation requires well organized and effective emergency planning. How quickly the event is responded and how efficiently response activities are managed are the main determinants of the overall costs of a disaster, both in terms of economic damages and fatalities. Remote sensing techniques play an important role in obtaining building damage information because of their non-contact, low cost, wide field of view, and fast response capacities. Now that more and diverse types of remote sensing data become available, various methods are designed and reported for building damage assessment. This paper provides a comprehensive review of these methods based on using optical images in three categories: mono, multi temporal and combination of images and vector map approach and also implements an automatic damage assessment method of buildings using high resolution satellite images and GIS layers. In this method, after extracting texture features of candidate buildings from both pre- and post-event images and defining optimized features, a neurofuzzy inference system was designed that determines buildings to four damage levels: Undamaged, Moderate damaged, Heavy damaged and Destroyed levels. Evaluation results show that the designed system has the overall accuracy of 89% in classifying buildings to the four damage levels.
