Automatic detection of ridges in lunar images using phase symmetry and phase congruency

Micheal, A. Ancy; Vani, K.; Sanjeevi, S.

doi:10.1016/j.cageo.2014.09.005

Cited by 9 publications

(4 citation statements)

References 13 publications

(23 reference statements)

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“…Thus, the dendrites were thought to be separated by a low density area. The ridge points ( Micheal, Vani & Sanjeevi, 2014 ) were determined based on the principal that modes lie on a ridge curve. Accordingly, we first found modes of , and constructed the ridge point sets passing through these modes by using them as initial points.…”

Section: Methodsmentioning

confidence: 99%

Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine

Wang

Chen

et al. 2016

PeerJ

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Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm–RTSVM— which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research.

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Section: Methodsmentioning

confidence: 99%

Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine

Wang

Chen

et al. 2016

PeerJ

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“…Lunar remote sensing optical images and Digital Elevation Models (DEMs) are the primary data sources for studying lunar tectonic activity and evolution. Previously, the detection of linear structures was primarily reliant on manual interpretation [1][2][3][4] and digitization [5][6][7][8], which was time-consuming and prone to significant errors. It was extremely challenging to manually detect global linear structures from a vast number of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Detecting Lunar Linear Structures Based on Multimodal Semantic Segmentation: The Case of Sinuous Rilles

Zhang,

Liu,

Michael

et al. 2024

Remote Sensing

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Tectonic features on the Moon can reflect the state of stress during the formation of the structure, and sinuous rilles can provide further insight into the tectonic-thermal evolution of the Moon. Manual visual interpretation is the primary method for extracting these linear structures due to their complex morphology. However, extracting these features from the vast amount of lunar remote sensing data requires significant time and effort from researchers, especially for small-scale tectonic features, such as wrinkle ridges, lobate scarps, and high-relief ridges. In order to enhance the efficiency of linear structure detection, this paper conducts research on the automatic detection method of linear structures using sinuous rilles as an example case. In this paper, a multimodal semantic segmentation method, “Sinuous Rille Network (SR-Net)”, for detecting sinuous rilles is proposed based on DeepLabv3+. This method combines advanced techniques such as ECA-ResNet and dynamic feature fusion. Compared to other networks, such as PSPNet, ResUNet, and DeepLabv3+, SR-Net demonstrates superior precision (95.20%) and recall (92.18%) on the multimodal sinuous rille test set. The trained SR-Net was applied in detecting lunar sinuous rilles within the range of 60°S to 60°N latitude. A new catalogue of sinuous rilles was generated based on the results of the detection process. The methodology proposed in this paper is not confined to the detection of sinuous rilles; with further improvements, it can be extended to the detection of other linear structures.

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“…As ridge is a symmetry feature that is invariant to illumination and contrast, Micheal et al (2014) proposed a technique based on phase symmetry and phase congruence for automatic ridge detection in lunar images, and the proposed approach provides better results than the plan curvature method. However, for some ridges that are difficult to identify from image, the method is limited.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Lunar Ridges Based on Dem Data

Peng

Wang

Yue

et al. 2019

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

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Wrinkle ridges are a common feature in the lunar maria and record subsequent contraction of mare infill. Automatic detection of wrinkle ridges is challenging because the ridges are of irregular shapes and many ridges have been eroded and/or degraded over time.The proposed method consists of the following steps. First, as the slope can reflect the gradient changes of the ridge rims to a certain extent, the slope map is generated and converted to a grayscale (0-255) image. Then the phase symmetry of the slope map is calculated with filter wavelength and filter scales parameters, which reduce the regions into symmetry regions. Next, a regional threshold is applied to limit the ridge candidates. Candidates with values less than the threshold are rejected. Moreover, the images are processed using a series of morphological operations, such as close, open, edge linking and noise removal. Finally, after thresholding the ridge map can be obtained. An experiment was performed using Lunar Reconnaissance Orbiter Camera (LROC) WAC image and topographic data from LOLA, the results demonstrate promising performance with detection percentage from 73 to 90.

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Automatic detection of ridges in lunar images using phase symmetry and phase congruency

Cited by 9 publications

References 13 publications

Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine

Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine

Detecting Lunar Linear Structures Based on Multimodal Semantic Segmentation: The Case of Sinuous Rilles

Automated Detection of Lunar Ridges Based on Dem Data

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