Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis

Radhika, Sudha; Tamura, Yukio; Matsui, Masahiro

doi:10.1016/j.jweia.2012.04.016

Cited by 21 publications

(19 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, apart from a feature representation strategy, the choice of features that best discriminate the damaged and non-damaged regions is also a key element. Numerous studies reported that textures are the most influential feature for damage pattern recognition, as the damaged regions tend to show uneven and peculiar texture patterns, in contrast to non-damaged regions [28][29][30]. Many damage classification studies used statistical textures such as grey level co-occurrence matrix (GLCM)-based features for the damage pattern recognition [10,31,32].…”

Section: Introductionmentioning

confidence: 99%

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

et al. 2016

View full text Add to dashboard Cite

Automatic post-disaster mapping of building damage using remote sensing images is an important and time-critical element of disaster management. The characteristics of remote sensing images available immediately after the disaster are not certain, since they may vary in terms of capturing platform, sensor-view, image scale, and scene complexity. Therefore, a generalized method for damage detection that is impervious to the mentioned image characteristics is desirable. This study aims to develop a method to perform grid-level damage classification of remote sensing images by detecting the damage corresponding to debris, rubble piles, and heavy spalling within a defined grid, regardless of the aforementioned image characteristics. The Visual-Bag-of-Words (BoW) is one of the most widely used and proven frameworks for image classification in the field of computer vision. The framework adopts a kind of feature representation strategy that has been shown to be more efficient for image classification-regardless of the scale and clutter-than conventional global feature representations. In this study supervised models using various radiometric descriptors (histogram of gradient orientations (HoG) and Gabor wavelets) and classifiers (SVM, Random Forests, and Adaboost) were developed for damage classification based on both BoW and conventional global feature representations, and tested with four datasets. Those vary according to the aforementioned image characteristics. The BoW framework outperformed conventional global feature representation approaches in all scenarios (i.e., for all combinations of feature descriptors, classifiers, and datasets), and produced an average accuracy of approximately 90%. Particularly encouraging was an accuracy improvement by 14% (from 77% to 91%) produced by BoW over global representation for the most complex dataset, which was used to test the generalization capability.

show abstract

Section: Introductionmentioning

confidence: 99%

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

et al. 2016

View full text Add to dashboard Cite

show abstract

“…ISODATA classifies imagery by grouping similar pixels together without pre-training samples, while training sample selection is essential for SVM. Texture has been widely applied for damage mapping after natural disasters (Vijayaraj et al 2008;Radhika et al 2012), and multivariate texture ( Bourgault and Marcotte 1991; Li et al 2009). One advantage of multivariate texture is that a specific band is not required for texture calculation, which is required by most traditional single-texture measures (Li et al 2009).…”

Section: Image Classificationmentioning

confidence: 99%

“…Additionally, Lidar has been used to detect three-dimensional variance after hurricanes (Li et al 2008;Hussain et al 2011) and tornadoes (Kashani et al 2014). Texture information derived from imagery (Vijayaraj et al 2008;Radhika et al 2012) has been used to rapidly detect damaged areas, as have band ratios (e.g., Normalized Difference Vegetation Index; NDVI) (Ill et al 1997;Womble et al 2006;Liou et al 2010;Wang and Xu 2010;. Multi-temporal analysis is generally employed for postdisaster events through the application of change detection (Al-Khudhairy et al 2005;Chen & Hutchinson 2007, 2010Butenuth et al 2011) for coregistered pre-and post-event images.…”

Section: Introductionmentioning

confidence: 99%

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

Jiang

Friedland

2015

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectralÀtextural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

show abstract

“…For more accurate classification the margin of separation must be as large as possible. Biorthogonal wavelet is proved to be the two-dimensional discrete wavelet with maximum margin of separation (Radhika et al, 2012).…”

Section: Best Wavelet Pattern Selectionmentioning

confidence: 99%

“…Various research have also been done on other natural disasters such as wild fires (Ambrosia et al, 1998), floods (Groeve and Riva, 2009), landslides (Danneels et al, 2008) by computational identification using low-resolution satellite images. Tornado damage path tracking has been accomplished from low-resolution satellite imagery by detecting changes from pre-and post-storm imageries (Soe et al, 2008;Thomas et al, 2002) and using post-storm imageries alone (Radhika et al, 2011(Radhika et al, , 2012. But the introduction of high-resolution satellite imageries has created a breakthrough in identification of disaster affected areas for rescue purposes as well as reconstruction of wind disaster damaged buildings.…”

Section: Introductionmentioning

confidence: 99%

Cyclone damage detection on building structures from pre- and post-satellite images using wavelet based pattern recognition

Radhika¹,

Tamura

Matsui

2015

Journal of Wind Engineering and Industrial Aerodynamics

Self Cite

View full text Add to dashboard Cite

Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis

Cited by 21 publications

References 16 publications

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

Cyclone damage detection on building structures from pre- and post-satellite images using wavelet based pattern recognition

Contact Info

Product

Resources

About