when an earthquake happens, the image-based techniques are influential tools for detection and classification of damaged buildings. Obtaining precise and exhaustive information about the condition and state of damaged buildings after an earthquake is basis of disaster management. Today's using satellite imageries such Quickbird is becoming more significant data for disaster management. In this paper, a method for detecting and classifying of damaged buildings using satellite imageries and digital map is proposed. In this method after extracting buildings position from digital map, they are located in the pre-event and post-event images of Bam earthquake. After generating features, genetic algorithm applied for obtaining optimal features. For classification, Adaptive boosting is used and compared with neural networks. Experimental results show that total accuracy of adaptive boosting for detecting and classifying of collapsed buildings is about 84 percent.
Analysis of changes in natural resources is one of the fundamental issues in remote sensing. Several research studies regarding the process of changes in natural resources using satellite imageries and image processing techniques have been done. Anzali pond is one of the important ecosystems in Iran that under the impact of some factors such as drought has the gradual drying trend over the last years. This study measures the area of basin surface and predicts the process of changes in the climate of the pond neighborhood during the next years, using GMDH neural network. Satellite imagery and meteorological data is used for this analysis. The final results represent reduction in area from 82 in 1998 to 51 in 2010. The average depth of the pond decreased to less than 4m in 2010 from 9m in 1998. The main reason for this reduction is diversion of rivers, sediment entering and changes in land use around the pond. If this trend continues, the amount of pollutants and toxins will reach to warning and this is a serious threat for animals and pond dwellers.
Abstract:Iranian ponds and water ecosystems are valuable assets which play decisive roles in economic, social, security and political affairs. Within the past few years, many Iranian water ecosystems such asUrmia Lake, Karoun River and Anzali Pond have been under disappearance threat. Ponds are habitats which cannot be replaced and this makes it necessary to investigate their changes in order to save these valuable ecosystems. The present research aims to investigate and evaluate the trend of variations in Anzali Pond using meteorological data between 1991-2010 by means of GMDH, which is based upon genetic algorithm and is a powerful technique in modeling complex dynamic non-linear systems, and linear regression technique. Input variables of both methodsinclude all factors (inside system and outside system factors) which affect variations in Anzali Pond. Exactness of linear regression method was 78% and exactness of GMDH neural network method was more than 97%. As as result, exactness of GMDH neural network method is significantly better than regression model.
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