Using object-oriented coupled deep learning approach for typical object inspection of transmission channel

Wei, Shengtao; Luo, Meng; Zhu, Li-Yuan; Liu, Yang

doi:10.1016/j.jag.2022.103137

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…In this study, based on the multiscale hierarchy constructed with the optimal segmentation scale (segmentation parameter = 30), the information from the Moso bamboo forest was extracted accurately with a producer's accuracy of 88.89% and user's accuracy of 86.96%. Zhang et al (2020) and Wei et al (2023) showed that the object-based method achieved improvement classification performance [5,50]. In comparison, Tan et al (2021) only utilizes object-based monitoring landslides without optimizing the segmentation scale.…”

Section: Discussionmentioning

confidence: 98%

“…The application of remote sensing data in combination with forest resource inventory data is an important way to study the forest carbon sink and its response to global climate change [1][2][3][4][5][6][7]. Carbon storage estimation using remote sensing data usually relies on understanding absorption, reflection, and transmission of solar radiation, as well as other associated mechanisms within the vegetation canopy and atmosphere.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

Han

2023

Remote Sensing

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Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable for Chinese forest management inventory. This research indicates that the construction of a SPOT-6 multiscale hierarchy with the 30 scale as the optimal segmentation scale achieves accurate information extraction for Moso bamboo forests. The producer’s and user’s accuracy are 88.89% and 86.96%, respectively. A random generalized linear model (RGLM), constructed using the multiscale hierarchy, can accurately estimate carbon storage of the bamboo forest in the study area, with a fitting and test accuracy (R2) of 0.74 and 0.64, respectively. In contrast, pixel-based methods using the RGLM model have a fitting and prediction accuracy of 0.24 and 0.01, respectively; thus, the object-based RGLM is a major improvement. The multiscale object hierarchy correctly analyzed the multiscale correlation and responses of bamboo forest elements to carbon storage. Objects at the 30 scale responded to the microstructure of the bamboo forest and had the strongest correlation between estimated carbon storage and measured values. Objects at the 60 scale did not directly inherit the forest information, so the response to the measured carbon storage of the bamboo forest was the smallest. Objects at the 90 scale serve as super-objects containing the forest feature information and have a significant correlation with the measured carbon storage. Therefore, in this study, a carbon storage estimation model was constructed based on the multiscale characteristics of the bamboo forest so as to analyze correlations and greatly improve the fitting and prediction accuracy of carbon storage.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

Han

2023

Remote Sensing

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“…However, this approach still relies on pixel-based categorization determinations, where classification is based on probabilities of different categories, potentially leading to the inclusion of noise and indistinct borders. Combining deep learning with OBIA, which focuses on identifying features based on objects rather than pixels, might enhance model accuracy [21,29,30]. Based on such assumptions, a series of studies was conducted.…”

Section: Discussionmentioning

confidence: 99%

“…The first approach is conducting OBIA to select and combine characteristics prior to inputting these data into a deep learning model for training [38,39], which was used in our study. The second approach implied employment of deep learning for initial image analysis, followed by merging the results with OBIA to enhance the ultimate recognition results [19,21]. In order to investigate more effective utilization of object-oriented approaches, we combined the two methods described above.…”

Section: Discussionmentioning

confidence: 99%

“…The integration of OBIA and deep learning can enhance the precision of semantic segmentation to a certain extent. This technique has been successfully applied in various applications, such as the extraction of vegetation [19], medical research [20], and the detection of buildings [21]. However, its application in identifying sloping cropland within a waterlogged area has not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Xie,

Wang,

Wang

et al. 2024

Sustainability

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Drainage difficulties in the waterlogged areas of sloping cropland not only impede crop development but also facilitate the formation of erosion gullies, resulting in significant soil and water loss. Investigating the distribution of these waterlogged areas is crucial for comprehending the erosion patterns of sloping cropland and preserving black soil resource. In this study, we built varied models based on two stages (one using only deep learning methods and the other combining object-based image analysis (OBIA) with deep learning methods) to identify waterlogged areas using high-resolution remote sensing data. The results showed that the five deep learning models using original remote sensing imagery achieved precision rates varying from 54.6% to 60.9%. Among these models, the DeepLabV3+-Xception model achieved the highest accuracy, as indicated by an F1-score of 53.4%. The identified imagery demonstrated a significant distinction in the two categories of waterlogged areas: sloping cropland erosion zones and erosion risk areas. The former had obvious borders and fewer misclassifications, exceeding the latter in terms of identification accuracy. Furthermore, the accuracy of the deep learning models was significantly improved when combined with object-oriented image analysis. The DeepLabV3+-MobileNetV2 model achieved the maximum accuracy, with an F1-score of 59%, which was 6% higher than that of the model using only original imagery. Moreover, this advancement mitigated issues related to boundary blurriness and image noise in the identification process. These results will provide scientific assistance in managing and reducing the impact in these places.

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Land use and cover change and influencing factor analysis in the Shiyang River Basin, China

Zhao,

Cao,

Sha

et al. 2024

J. Arid Land

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Using object-oriented coupled deep learning approach for typical object inspection of transmission channel

Cited by 7 publications

References 25 publications

Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Land use and cover change and influencing factor analysis in the Shiyang River Basin, China

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