Object-Oriented Image Understanding and Post-Earthquake Damage Assessment for the 2003 Bam, Iran, Earthquake

Gusella, Luca; Adams, Beverley J.; Bitelli, Gabriele; Huyck, Charles; Mognol, Alessandro

doi:10.1193/1.2098629

Cited by 74 publications

(24 citation statements)

References 3 publications

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“…They used a creative shadow-based filter on fine spatial resolution imagery with a watershed segmentation algorithm to detect and map damaged or undamaged buildings. Gusella et al [115] quantified the number of buildings that collapsed following the 2003 Bam earthquake, by first segmenting pre-earthquake Quickbird imagery and then comparing the spectral signature of those objects in the post-earthquake imagery. They report an overall accuracy of 70% for the damage classification.…”

Section: Review Of Obia and Public Health Applicationsmentioning

confidence: 99%

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

et al. 2011

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Abstract:The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or "OBIA") can provide advantages for public health research. Rather than classifying each pixel based on its spectral content alone, the OBIA approach first segments an image into objects, or segments, based on spatially connected pixels with similar spectral properties, and then these objects are classified based on their spectral, spatial and contextual attributes as well as by their interrelations across scales. The approach can lead to increases in classification accuracy, and it can also develop multi-scale topologies between objects that can be utilized to help understand human-disease-health systems. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.

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Section: Review Of Obia and Public Health Applicationsmentioning

confidence: 99%

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

et al. 2011

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“…More quantitative and details of damages are expected from the damage analysts and disaster management practitioners. Researches in response to recent disaster events showed that detailed information derived from very high-resolution satellite images could accommodate their requirements (Chesnel et al 2007;Gusella et al 2005;Saito et al 2004;Vu and Ban, 2010).…”

Section: Introductionmentioning

confidence: 99%

Rapid Disaster Damage Estimation

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The experiences from recent disaster events showed that detailed information derived from high-resolution satellite images could accommodate the requirements from damage analysts and disaster management practitioners. Richer information contained in such high-resolution images, however, increases the complexity of image analysis. As a result, few image analysis solutions can be practically used under time pressure in the context of post-disaster and emergency responses. To fill the gap in employment of remote sensing in disaster response, this research develops a rapid high-resolution satellite mapping solution built upon a dual-scale contextual framework to support damage estimation after a catastrophe. The target objects are building (or building blocks) and their condition. On the coarse processing level, statistical region merging deployed to group pixels into a number of coarse clusters. Based on majority rule of vegetation index, water and shadow index, it is possible to eliminate the irrelevant clusters. The remaining clusters likely consist of building structures and others. On the fine processing level details, within each considering clusters, smaller objects are formed using morphological analysis. Numerous indicators including spectral, textural and shape indices are computed to be used in a rule-based object classification. Computation time of raster-based analysis highly depends on the image size or number of processed pixels in order words. Breaking into 2 level processing helps to reduce the processed number of pixels and the redundancy of processing irrelevant information. In addition, it allows a data-and tasks-based parallel implementation. The performance is demonstrated with QuickBird images captured a disaster-affected area of Phanga, Thailand by the 2004 Indian Ocean tsunami are used for demonstration of the performance. The developed solution will be implemented in different platforms as well as a web processing service for operational uses.

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“…Object Based Image Analysis (OBIA) was used previously to map forests (Chubey, M.S. et al 2006), natural catastrophes (effects of an earthquake: Gusella, L. et al 2005) and cities as well (Chen, Y. et al 2007). It has been established that under appropriate conditions OBIA could be more accurate than pixel-based methods (Al Khudairy, D.H. et al 2005).…”

Section: Introductionmentioning

confidence: 99%

The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

et al. 2017

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High spatial and spectral resolution aerial images make it possible to develop detailed and large-scale (about 1:5,000) urban land cover maps. The main objectives of this study are (1) to evaluate the correlation between laboratory and hyperspectral image spectra to select proper bands and training samples for classification; (2) to develop a classification process to combine the spectral and spatial information of multispectral and hyperspectral images and make an urban land cover map for the study area in Szeged, Hungary; and (3) to examine the effect of different roof types on the modification of surface temperature. Reference materials were collected from the training area and their spectral characteristics were measured by a laboratory spectrometer. The hyperspectral image and laboratory spectral data between 500-800 nm showed a very strong correlation, the correlation coefficient was 0.99. The urban land cover map was produced by the combination of segmentation procedure and Spectral Angle Mapper (SAM) method using the spatial information derived from multispectral image and the spectral information of the hyperspectral image. Eight land cover classes were identified as impervious surfaces (asphalt, 4 types of tiled roof), water, and green vegetation. The overall accuracy of urban land cover map was 87.9 per cent. According to the results, an accurate large-scale urban land cover map can be generated from the fusion of multispectral and hyperspectral images. We presented that certain roof types have significant effect on surface temperature, which is strongly connected to the urban heat island phenomenon, and influences population health.

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Object-Oriented Image Understanding and Post-Earthquake Damage Assessment for the 2003 Bam, Iran, Earthquake

Cited by 74 publications

References 3 publications

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

Rapid Disaster Damage Estimation

The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

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