Multifractal Approach for Seafloor Characterization

Chakraborty, Bhaskar; Haris, K.; Latha, G.; Maslov, Nicolas; Menezes, Andrew

doi:10.1109/lgrs.2013.2245856

Cited by 27 publications

(6 citation statements)

References 27 publications

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“…The definition of D q was formulated in the context of analysis of natural measure of chaotic attractors by Grassberger [1983] and Hentschel and Procaccia [1983]. However, we emphasize that D q can also be applied to any measures [Ott, 1993, chapter 9], what was confirmed by numerous studies [Meneveau and Sreenivasan, 1991;Wawrzaszek and Macek, 2010;Chakraborty et al, 2014;Wawrzaszek et al, 2014]. In the frame o this work for the direct estimation of the function D q from the experimental data we use the Box-Counting Based Moment method [Halsey et al, 1986], presented together with schematic diagram in Wawrzaszek et al [2014].…”

Section: Degree Of Multifractalitymentioning

confidence: 92%

Applicability of multifractal features as global characteristics of WorldView-2 panchromatic satellite images

Drzewiecki

Wawrzaszek

Krupiński

et al. 2016

European Journal of Remote Sensing

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In this work we analyse fractal and multifractal characteristics for description and extraction of information from VHR satellite images. We propose the degree of multifractality as a global descriptor of satellite image content and investigate its usefulness for classification of WorldView-2 image chips into main land cover types. The research confirmed that it is possible to use the textural features as efficient global descriptors of WorldView-2 panchromatic image content. Results show that the degree of multifractality is related to land cover type prevailing in the imaged area. It was also proved that multifractal parameters should be considered as valuable textural features in the context of land cover classification.

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Section: Degree Of Multifractalitymentioning

confidence: 92%

Applicability of multifractal features as global characteristics of WorldView-2 panchromatic satellite images

Drzewiecki

Wawrzaszek

Krupiński

et al. 2016

European Journal of Remote Sensing

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“…Fractal analysis is a relatively novel mathematical tool that has been used successfully in many fields including biology and physical sciences. [14][15][16][17] However, its application in the NDE/structural health monitoring (SHM) community has been modest. [18][19][20][21] Recently, the authors have used a monofractal analysis to discriminate different damage grades (DGs) in RCSWs.…”

Section: Introductionmentioning

confidence: 99%

Multifractal analysis of crack patterns in reinforced concrete shear walls

Ebrahimkhanlou

Farhidzadeh

Salamone

2016

Structural Health Monitoring

105

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Conventionally, the assessment of reinforced concrete shear walls relies on manual visual assessment which is time-consuming and depends heavily on the skills of the inspectors. The development of automated assessment employing flying and crawling robots equipped with high-resolution cameras and wireless communications to acquire digital images and advance image processing to extract crack patterns has paved the path toward implementing an automated system which determines structural damage based on visual signals acquired from structures. Since there are few, if any, studies to correlate crack patterns to structural integrity, this article proposes to analyze crack patterns using a multifractal analysis. The approach is initially tested on synthetic crack patterns, and then it is applied to a set of experimental data collected during the testing of two large-scale reinforced concrete shear wall subjected to controlled reversed cyclic loading. The structural response data available for each specimen are used to link the multifractal parameters with the structural performance of the two specimens. A relationship between the multifractal parameters and the crack patterns’ evolution and mechanism is noted. The results show that as the crack patterns extend and grow, multifractal parameters move toward higher values. The parameters jump as the mechanical response shows severe stiffness loss. In this study, no attempt is made to automate the process of mapping cracks from images.

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“…τ(q) denotes the sequence of mass exponents estimated by the slope of the fitted function ln X q (ε) vs ln(ε). The value of q reflects the importance of the subset of the distribution ratio Q i (ε) in the partition function [57]. When q→+∞ the maximum probability plays a significant role.…”

Section: The Multifractal Methodsmentioning

confidence: 99%

Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

Zhang

Loáiciga

Liao

et al. 2021

IJGI

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Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

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Multifractal Approach for Seafloor Characterization

Cited by 27 publications

References 27 publications

Applicability of multifractal features as global characteristics of WorldView-2 panchromatic satellite images

Applicability of multifractal features as global characteristics of WorldView-2 panchromatic satellite images

Multifractal analysis of crack patterns in reinforced concrete shear walls

Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

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