scholarly journals APPLICATION OF OFF-NADIR SATELLITE IMAGERY IN EARTHQUAKE DAMAGE ASSESSMENT USING OBJECT-BASED HOG FEATURE DESCRIPTOR

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 167-171
Author(s):  
S. Jabari ◽  
M. Krafczek
Keyword(s):  
Change Detection ◽  
Damage Detection ◽  
Disaster Management ◽  
Satellite Images ◽  
Feature Descriptor ◽  
Object Based ◽  
Before And After ◽  
Map Generation ◽  
Spectral Comparison ◽  
Damage Map

<p><strong>Abstract.</strong> One of the most crutial applications of very-high-resolution (VHR) satellite images is disaster management. In disaster management, time is of great importance. Therefore, it is vital to acquire satellite images as quickly as possible and benefit from automatic change detection to speed up the process. Automatic damage map generation is performed by overlaying the co-registered before and after images of the area of interest and, compring them to highlight the affected infrastructures. For speeding up image capture, satellites tilt their imaging sensor and take images from oblique angles. However, this kind of image acquisition causes severe geometric distortion in the images, which hinders image co-registration in automatic change detection. In this study, a Patch-Wise Co-Registration (PWCR) solution is used. In this algorithm, the before and after images are co-registered in a segment-by-segment manner. From the literature, this algorithm is followed by a spectral comparison to detect changes. However, due to the complicated structure of debris in damage detection applications, spectral comparison methods cannot perform well. In this work, we developed an object-based method using Histogram of Oriented Gradient descriptor to detect damges and compared our results to different existing spectral and textural change detection methods. The algorithm is tested on images from the 2010-Heidi earthquake, captured by DigitalGlobe. The achieved highly accurate results demonstrate the potential of using off-nadir remote sensing images for automatic urban damage detection possibly in early response systems as it speeds up the damage map generation by providing flexibility to utilize images taken from different anlges.</p>

scholarly journals Accelerate disaster risk mapping using an automated zonal change detection model for loss estimation and recovery: a flood case study

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ali Amasha
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Flash Flood ◽  
Disaster Risk ◽  
Loss Estimation ◽  
Risk Mapping ◽  
Detection Model ◽  
Applied Model ◽  
High Resolution Satellite Images ◽  
Before And After

Abstract Background The flash flood still constitutes one of the major natural meteorological disasters harmfully threatening local communities, that creates life losses and destroying infrastructures. The severity and magnitude of disasters always reflected from the size of impacts. Most of the conventional research models related to flooding vulnerability are focusing on hydro-meteorological and morphometric measurements. It, however, requires quick estimate of the flood losses and assess the severity using reliable information. An automated zonal change detection model applied, using two high-resolution satellite images dated 2009 and 2011 coupled with LU/LC GIS layer, on western El-Arish City, downstream of Wadi El-Arish basin. The model enabled to estimate the severity of a past flood incident in 2010. Results The model calculated the total changes based on the before and after satellite images based on pixel-by-pixel comparison. The estimated direct-damages nearly 32,951 m2 of the total mapped LU/LC classes; (e.g., 11,407 m2 as 3.17% of the cultivated lands; 6031 m2 as 7.22% of the built-up areas and 4040 m2 as 3.62% of the paved roads network). The estimated cost of losses, in 2010 economic prices for the selected three LU/LC classes, is nearly 25 million USD, for the cultivation fruits and olives trees, ~ 4 million USD for built-up areas and ~ 1 million USD for paved roads network. Conclusion The disasters’ damage and loss estimation process takes many detailed data, longtime, and costed as well. The applied model accelerates the disaster risk mapping that provides an informative support for loss estimation. Therefore, decision-makers and professionals need to apply this model for quick the disaster risks management and recovery.

Object-based rapid change detection for disaster management

2011 ◽  
Author(s):  
Holger Thunig ◽  
Ulrich Michel ◽  
Manfred Ehlers ◽  
Peter Reinartz
Keyword(s):  
Change Detection ◽  
Disaster Management ◽  
Rapid Change ◽  
Object Based

scholarly journals PROPOSAL OF CHANGE DETECTION IN LAND COVER FOR PIPELINE MONITORING FROM RAPIDEYE AND IKONOS IMAGES

2014 ◽  
Vol 32 (4) ◽  
pp. 655 ◽  
Author(s):  
Paulina Setti Riedel ◽  
Mara Lúcia Marques ◽  
Mateus Vidotti Ferreira ◽  
Marcelo Elias Delaneze
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Satellite Images ◽  
Bare Soil ◽  
Reference Limit ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Pipeline Monitoring ◽  
Structural Preservation

ABSTRACT. The goal of this study was to improve and evaluate the applicability of a methodological procedure of pipeline monitoring to reveal indicators of thirdparty activities that may interfere with the structural preservation of pipes and environmental damages. The procedure was developed from the technique of changedetection through object-based classification of land cover, using high resolution satellite images applied to a section of the Guararema-Mauá – São Paulo pipeline, Brazil. In the seven-month monitoring period performed with RapidEye imaging, an area of 2.024 km2 was identified as area of change, corresponding to 3.30% of thetotal area analyzed. For the monitoring performed with Ikonos imaging during a four-month period, changes were detected in an area of 0.187 km2, which correspondedto 1.92% of the total area analyzed. The main changes in land cover were from Bare Soil to Grassland, due to changes related to the different stages of agriculturalactivity and reforestation areas, as well as the natural regeneration of vegetation over the pipeline and solid waste landfill. The results of the change detection of landcover from object-based classification were close to the technique reference limit for areas with great complexity and diversity of space occupation.Keywords: structural preservation of pipes, object-based classification, high resolution satellite images. RESUMO. Este estudo teve por objetivo avaliar a aplicabilidade de um procedimento metodológico de monitoramento de faixas de dutos que revelem indicativos deatividades de terceiros que podem interferir na integridade estrutural dos dutos e provocar danos ambientais. O procedimento foi desenvolvido a partir da técnica dedetecção de mudanças na cobertura da terra pela classificação baseada no objeto, com utilização de imagens orbitais de alta resolução. Este procedimento foi empregadoem um trecho da faixa de dutos Guararema-Mauá – SP, no monitoramento realizado por meio de imagens RapidEye. Em um período de sete meses, foram identificados 2,024 km2 como área de mudança, que corresponde a 3,30% do total da área analisada. Para o monitoramento realizado a partir da imagem Ikonos, com período de quatro meses, foi identificada como mudança uma área de 0,187 km2, correspondendo a 1,92% do total da área analisada. As principais mudanças ocorridas foramentre Solo Exposto e Vegetaçao Rasteira, devido às alterações ocorridas nos estágios de cultivo agrícola e áreas de reflorestamento, como também, estão associadas às áreas de regeneração da vegetação da faixa de dutos e aterro sanitário. Os resultados da detecção de mudanças da cobertura da terra pela classificação baseada no objeto atingiram acertos próximos ao limite de para esta técnica, em áreas com grande complexidade e diversidade de ocupação do espaço.Palavras-chave: integridade estrutural dos dutos, classificação baseada no objeto, imagens orbitais de alta resolução.

scholarly journals Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure

2020 ◽  
Vol 12 (21) ◽  
pp. 3606
Author(s):  
Yi Tian ◽  
Ming Hao ◽  
Hua Zhang
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Spectral Information ◽  
Feature Descriptor ◽  
Shape Similarity ◽  
Local Spectrum ◽  
Remote Sensing Images ◽  
Shape Context ◽  
Spot 5 ◽  
Very High

The emergence of very high resolution (VHR) images contributes to big challenges in change detection. It is hard for traditional pixel-level approaches to achieve satisfying performance due to radiometric difference. This work proposes a novel feature descriptor that is based on spectrum-trend and shape context for VHR remote sensing images. The proposed method is mainly composed of two aspects. The spectrum-trend graph is generated first, and then the shape context is applied in order to describe the shape of spectrum-trend. By constructing spectrum-trend graph, spatial and spectral information is integrated effectively. The approach is performed and assessed by QuickBird and SPOT-5 satellite images. The quantitative analysis of comparative experiments proves the effectiveness of the proposed technique in dealing with the radiometric difference and improving the accuracy of change detection. The results indicate that the overall accuracy and robustness are both boosted. Moreover, this work provides a novel viewpoint for discriminating changed and unchanged pixels by comparing the shape similarity of local spectrum-trend.

scholarly journals OBJECT-ORIENTED ANALYSIS OF SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS FOR POST-EARTHQUAKE BUILDINGS CHANGE DETECTION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 139-144
Author(s):  
N. Khodaverdi zahraee ◽  
H. Rastiveis
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Feature Vector ◽  
Human Life ◽  
Textural Features ◽  
Image Objects ◽  
Input Feature ◽  
High Resolution Satellite Images ◽  
Before And After ◽  
Artificial Neural

Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged area, the amount and type of damage can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. The purpose of this paper is to propose and implement an automatic approach for mapping destructed buildings after an earthquake using pre- and post-event high resolution satellite images. In the proposed method after preprocessing, segmentation of both images is performed using multi-resolution segmentation technique. Then, the segmentation results are intersected with ArcGIS to obtain equal image objects on both images. After that, appropriate textural features, which make a better difference between changed or unchanged areas, are calculated for all the image objects. Finally, subtracting the extracted textural features from pre- and post-event images, obtained values are applied as an input feature vector in an artificial neural network for classifying the area into two classes of changed and unchanged areas. The proposed method was evaluated using WorldView2 satellite images, acquired before and after the 2010 Haiti earthquake. The reported overall accuracy of 93% proved the ability of the proposed method for post-earthquake buildings change detection.

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