Application of edge detection to potential field data using eigenvalue analysis of structure tensor

Sertçelik, İbrahim; Kafadar, Özkan

doi:10.1016/j.jappgeo.2012.06.005

Cited by 45 publications

(14 citation statements)

References 21 publications

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“…The horizontal gravity gradient method and vertical gravity gradient method (e.g., Evjen 1936;Elkins 1951;Tsuboi and Kato 1952;Blakely and Simpson 1986) are classic edge emphasis techniques. In recent years, higher and keener extraction techniques have been suggested (e.g., Miller and Singh 1994;Cooper and Cowan 2006;Sertcelik and Kafadar 2012;Zhang et al 2014). In addition, attention has been paid to techniques that evaluate the shape of the potential field (e.g., Koenderink and van Doorn 1992;Robert 2001;Zhou et al 2013;Cevallos 2014).…”

Section: Introductionmentioning

confidence: 99%

Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

Kusumoto

2017

Prog. in Earth and Planet. Sci.

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The dips of boundaries in faults and caldera walls play an important role in understanding their formation mechanisms. The fault dip is a particularly important parameter in numerical simulations for hazard map creation as the fault dip affects estimations of the area of disaster occurrence. In this study, I introduce a technique for estimating the fault dip using the eigenvector of the observed or calculated gravity gradient tensor on a profile and investigating its properties through numerical simulations. From numerical simulations, it was found that the maximum eigenvector of the tensor points to the high-density causative body, and the dip of the maximum eigenvector closely follows the dip of the normal fault. It was also found that the minimum eigenvector of the tensor points to the low-density causative body and that the dip of the minimum eigenvector closely follows the dip of the reverse fault. It was shown that the eigenvector of the gravity gradient tensor for estimating fault dips is determined by fault type. As an application of this technique, I estimated the dip of the Kurehayama Fault located in Toyama, Japan, and obtained a result that corresponded to conventional fault dip estimations by geology and geomorphology. Because the gravity gradient tensor is required for this analysis, I present a technique that estimates the gravity gradient tensor from the gravity anomaly on a profile.

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Section: Introductionmentioning

confidence: 99%

Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

Kusumoto

2017

Prog. in Earth and Planet. Sci.

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“…Meanwhile, inspired by the successful applications of closed contour extraction, such as Chan-Vese model in ship detection [20,21], a novel contour measure based on GMR and eigenvalue analysis of structure tensor is presented to characterize candidate ship targets and eliminate residual clutter simultaneously. Structure tensor has become a powerful tool for edge analysis, and achieved some impressive results for its good perceptivity on the dominant direction at the local image [39,40]. To investigate the local contour information of TIR ship targets, the structure tensor ST is firstly calculated as:…”

Section: Contour Description Of Tir Ship Based On Eigenvalue Analysismentioning

confidence: 99%

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

Zhu

et al. 2019

Applied Sciences

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The existing thermal infrared (TIR) ship detection methods may suffer serious performance degradation in the situation of heavy sea clutter. To cope with this problem, a novel ship detection method based on morphological reconstruction and multi-feature analysis is proposed in this paper. Firstly, the TIR image is processed by opening- or closing-based gray-level morphological reconstruction (GMR) to smooth intricate background clutter while maintaining the intensity, shape, and contour features of ship target. Then, considering the intensity and contrast features, the fused saliency detection strategy including intensity foreground saliency map (IFSM) and brightness contrast saliency map (BCSM) is presented to highlight potential ship targets and suppress sea clutter. After that, an effective contour descriptor namely average eigenvalue measure of structure tensor (STAEM) is designed to characterize candidate ship targets, and the statistical shape knowledge is introduced to identify true ship targets from residual non-ship targets. Finally, the dual method is adopted to simultaneously detect both bright and dark ship targets in TIR image. Extensive experiments show that the proposed method outperforms the compared state-of-the-art methods, especially for infrared images with intricate sea clutter. Moreover, the proposed method can work stably for ship target with unknown brightness, variable quantities, sizes, and shapes.

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“…(f) Tilt angle map of the synthetic data [Colour figure can be viewed at wileyonlinelibrary.com] direction of quasi-two-dimensional (2D) sources can be inferred from the eigenvector corresponding to the smallest eigenvalue of the tensor matrix. Sertcelik and Kafadar (2012) have used the structure tensor technique to determine edges of the subsurface lineaments from potential field data using eigenvalues. Oruç, Sertçelik, Kafadar, and Selim (2013) have computed eigenvalues and determinant of the curvature gravity gradient tensor (CGGT) in order to interpret geological boundaries.…”

Section: Introductionmentioning

confidence: 99%

Edge detection for the buried archaeological structures with the geophysical image processing method in the Alabanda Ancient Cistern in Turkey

Karaaslan

2020

Archaeological Prospection

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Edge detection techniques are some of the most useful techniques to determine the lineaments of buried structures from magnetic data. In this context, many techniques have been designed by using the magnetic gradient tensor (MGT). In this work, eigenvalue analysis of the full MGT and a new normalized approach [normalized edge detector (NK)], which balances strong and weak amplitude anomalies simultaneously, was utilized to outline lineaments of the buried archaeological structures. Satisfactory results were obtained from noisy and noise free synthetic data which were generated from prism models. The techniques were implemented on the archaeological magnetic data acquired from a buried ancient cistern. In addition, three‐dimensional (3D) electrical resistivity tomography (ERT) inversion sections and two‐dimensional (2D) magnetic inversion section and tilt angle methods were used to compare them against the results of edge detection analysis. The results of the normalized eigenvalue analysis agreed closely with all techniques in detecting the lineaments of the buried archaeological structure. NK detected the lineaments of the buried structure more clearly than the tilt angle and had better resolution. All of the results have shown that eigenvalue analysis of the full MGT and especially the newly introduced approach NK can provide important information for archaeological studies.

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Application of edge detection to potential field data using eigenvalue analysis of structure tensor

Cited by 45 publications

References 21 publications

Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

Edge detection for the buried archaeological structures with the geophysical image processing method in the Alabanda Ancient Cistern in Turkey

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