Performance Evaluation of Jaccard-Dice  Coefficient on Building Segmentation from  High Resolution Satellite Images

Ataş, İsa

doi:10.17694/bajece.1212563

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…FCN (fully convolutional network): A foundational model for semantic segmentation that replaces fully connected layers with convolutional layers to enable pixel-level predictions [27].…”

Section: 2 Performance Analysis and Comparisonmentioning

confidence: 99%

Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges

Wang,

Zhou

et al. 2024

Forests

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With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred boundaries of flames and smoke, traditional convolutional neural networks (CNNs) face limitations in forest fire image segmentation, including flame edge recognition, class imbalance issues, and adapting to complex scenarios. This study aims to enhance the accuracy and efficiency of flame recognition in forest fire images by introducing a backbone network based on the Swin Transformer and combined with an adaptive multi-scale attention mechanism and focal loss function. By utilizing a rich and diverse pre-training dataset, our model can more effectively capture and understand key features of forest fire images. Through experimentation, our model achieved an intersection over union (IoU) of 86.73% and a precision of 91.23%. This indicates that the performance of our proposed wildfire segmentation model has been effectively enhanced. A series of ablation experiments validate the importance of these technological improvements in enhancing model performance. The results show that our approach achieves significant performance improvements in forest fire image segmentation tasks compared to traditional models. The Swin Transformer provides more refined feature extraction capabilities, the adaptive multi-scale attention mechanism helps the model focus better on key areas, and the focal loss function effectively addresses the issue of class imbalance. These innovations make the model more precise and robust in handling forest fire image segmentation tasks, providing strong technical support for future forest fire monitoring and prevention.

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“…FCN (fully convolutional network): A foundational model for semantic segmentation that replaces fully connected layers with convolutional layers to enable pixel-level predictions [27].…”

Section: 2 Performance Analysis and Comparisonmentioning

confidence: 99%

Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges

Wang,

Zhou

et al. 2024

Forests

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Vision transformer and CNN-based skin lesion analysis: classification of monkeypox

Yolcu Oztel

2024

Multimed Tools Appl

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Monkeypox is an important health problem. Rapid diagnosis of monkeypox skin lesions and emergency isolation when necessary is essential. Also, some skin lesions, such as melanoma, can be fatal and must be rapidly distinguished. However, in some cases, it is difficult to distinguish the lesions visually. Methods such as dermoscopy, high-resolution ultrasound imaging, etc. can be used for better observation. But these methods are often based on qualitative analysis, subjective and time-consuming. Therefore, in this study, a quantitative and objective classification tool has been developed to assist dermatologists and scientists. The proposed system classifies seven skin lesions, including monkeypox. A popular approach Vision Transformer and some popular deep learning convolutional networks have been trained with the transfer learning approach and all results have been compared. Then, the models that show the best accuracy score have been combined to make the final prediction using bagging-ensemble learning. The proposed ensemble-based system produced 81.91% Accuracy, 65.94% Jaccard, 87.16% Precision, 74.12% Recall, and 78.16% Fscore values. In terms of different criteria metrics, the system produced competitive or even better results than the literature.

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Automation of Dice (Czekanowski-Sørensen) similarity index calculations in phyсological research

Bren,

Solonenko

et al. 2024

Algologia

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This paper examines the trends in the use of the Dice (Czekanowski-Sørensen) similarity index in studies of algae and cyanoprokaryotes. A concise overview of the characteristics of this metric is provided, considering its positive aspects and limitations. The relevance of the work is justified by the researchers' need for automation of Dice index calculations and the construction of resulting matrices. The article proposes a method for automating calculations using macros in the Excel environment. The authors provide an overview of the possibilities of this approach and offer their own macro for fast and convenient calculation of the Dice index without the need for third-party programs or formulas.

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Performance Evaluation of Jaccard-Dice Coefficient on Building Segmentation from High Resolution Satellite Images

Cited by 4 publications

References 28 publications

Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges

Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges

Vision transformer and CNN-based skin lesion analysis: classification of monkeypox

Automation of Dice (Czekanowski-Sørensen) similarity index calculations in phyсological research

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