Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold

Chen, Chao; Hua, Ke; Wu, Bin

doi:10.3934/era.2023066

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Image segmentation, an integral component in image processing, lays the technical groundwork for condition diagnosis by isolating image regions with varying characteristics [13]. While traditional segmentation methods primarily leverage threshold setting, histogram analysis [14], region growing, fuzzy clustering [15,16], K-means clustering [17], and edge detection [18,19], advanced techniques incorporate active contours, graph cuts, and sophisticated mathematical and probabilistic models [20]. Notably, deep learning approaches [21][22][23][24][25] like Fully Convolutional Networks (FCN) [26],U-Net [27],PSPNet [28] and FC-DenseNet have revolutionized segmentation with their high precision in pixel-level classification.…”

Section: Image Segmentationmentioning

confidence: 99%

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Xie,

Wu,

Tang

et al. 2024

PLoS ONE

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Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.

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Section: Image Segmentationmentioning

confidence: 99%

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Xie,

Wu,

Tang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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Review of the Accuracy of Satellite Remote Sensing Techniques in Identifying Coastal Aquaculture Facilities

Chen,

Lv,

Zhang

et al. 2024

Fishes

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The predominant form of aquaculture is the facility fishery, which is also subject to significant impacts from marine disasters. Conducting research on the extraction of facility fishery areas based on remote sensing technology is crucial to efficiently comprehending the configuration of coastal culture patterns and to establishing scientifically sound plans for managing and administering these areas. The extensive dispersion of facility fishery areas in coastal regions poses a challenge to the conduction of comprehensive field surveys. The utilization of satellite remote sensing images for information extraction has emerged as a significant area of research in the fields of coastal fishery and ecological environment. This study provides a systematic description of the current research status of coastal fishery area extraction methods using remote sensing technology from 2000 to 2022 reported in the literature. The methods discussed include the visual interpretation method, image element-based classification, object-based classification, supervised classification, unsupervised classification, and neural network classification. The extraction accuracy of each method in the coastal facility fishery area is evaluated, and the advantages and disadvantages of these methods, as well as their limitations and existing problems, are analyzed in detail, to construct a reference framework for the investigation of the high-precision extraction of facility fishery areas from satellite remote sensing images.

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Isolating switch state detection system based on depth information guidance

Xu,

Zhao,

Yin

et al. 2024

era

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<abstract> <p>This study addressed the critical role of isolating switches in controlling circuit connections for the stable operation of the substation. Our research introduced an innovative state detection system that utilized depth information guidance, comprising a controllable pan-tilt mechanism, a depth camera, and an industrial computer. The software component employed a two-stage strategy for precise isolating switch detection. Initially, the red green blue with depth (RGB-D) saliency network identified the approximate area of the isolating switch target. Subsequently, a fully connected conditional random field was applied to extract accurate detection results. The real-time state of the isolating switch was determined based on the geometric relationship between its arms. This approach enhanced the accuracy of isolating switch detection, ensuring practical applicability in engineering scenarios. The significance of this research lies in its contribution to advancing isolating switch monitoring through depth information guidance, promoting a more robust and reliable power system. The key improvement is implementing a two-stage strategy, combining RGB-D saliency analysis and conditional random field processing, resulting in enhanced accuracy in isolating switch detection. As validated through extensive experiments, the proposed system's successful application in practical engineering underscores its effectiveness in meeting the accuracy requirements for isolating switch detection and state detection. This innovation holds promise for broader applications in power systems, showcasing its potential to elevate the reliability and efficiency of electrical networks. Code of the proposed system is available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/miaomiao0909/Isolating-Switch-Detection/tree/master">https://github.com/miaomiao0909/Isolating-Switch-Detection/tree/master</ext-link>.</p> </abstract>

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Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold

Cited by 4 publications

References 45 publications

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Review of the Accuracy of Satellite Remote Sensing Techniques in Identifying Coastal Aquaculture Facilities

Isolating switch state detection system based on depth information guidance

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