Active Fire Detection Using a Novel Convolutional Neural Network Based on Himawari-8 Satellite Images

Hong, Zhonghua; Tang, Zhizhou; Pan, Haiyan; Zhang, Yuewei; Zheng, Zhiyao; Zhou, Ri-Gui; Ma, Zhenling; Zhang, Yun; Han, Yanling; Wang, Jing; Yang, Shuhu

doi:10.3389/fenvs.2022.794028

Cited by 29 publications

(20 citation statements)

References 55 publications

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“…Data obtained from different platforms can provide diverse and complementary information [46]- [47]. The smoke datasets for optical satellite images mostly come from lowresolution multi-spectral images such as MODIS [48], Himawari-8 [49], LandSat-8 [50], and GOES-16 [51]. The lack of large-scale, open-source, high-resolution labeled datasets for segmentation restricts the development of smoke segmentation network models for high-resolution optical satellite imagery.…”

Section: B Deep-learning-based Smoke Segmentation Methodsmentioning

confidence: 99%

AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network

Wang

Hong

Han

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Smoke is more observable than open fires. Optical satellite video has the advantages of a wide monitoring range, fast response speed, and good economy in large-scale surface smoke monitoring tasks. It can be used in wide-area forest wildfire monitoring, battlefield dynamic monitoring, disaster relief decision-making. The smoke segmentation method based on traditional handcrafted features is easily limited by the scene and data. This paper introduces the deep learning method to the optical satellite video smoke target segmentation. However, due to the lack of real smoke images and the blurred edges of smoke, there are currently few labeled datasets for smoke segmentation in high-resolution optical satellite imagery scenes, which cannot provide sufficient training data for deep learning models. The smoke image from the satellite perspective also has the characteristics of multi-scale features and ground object background interference. To solve the above problems, we construct a set of high-resolution optical satellite imagery smoke synthesis datasets based on the optical imaging process of smoke targets, which saves the cost of manual labeling. In addition, we design an attention-guided optical satellite video smoke segmentation network model (AOSVSSNet), which can effectively suppress the ground object background's false alarm and extract the smoke's multi-scale features. Synthetic data faces the transferability problem in real-world applications, so the physical constraints of the smoke imaging process are introduced into the loss function to improve the generalization of the model in real smoke data. The comprehensive evaluation results show that the method outperforms representative semantic segmentation networks.

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Section: B Deep-learning-based Smoke Segmentation Methodsmentioning

confidence: 99%

AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network

Wang

Hong

Han

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Te temporal and spatial dynamic fre textures were analyzed in [20] using 2D and 3D wavelet fragmentation. In addition, the authors in [21][22][23][24][25][26] discussed machine learning and deep learning methods for detecting forest fres. Tese studies highlight the diverse range of techniques and algorithms employed in fre detection research, showcasing advancements in accuracy, efciency, and real-time performance.…”

Section: Integration With Iot and Smartmentioning

confidence: 99%

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Vaegae,

Annepu,

Bagadi

et al. 2024

Journal of Optimization

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As smart cities expand rapidly, the demand for strong fire protection has become even more essential. A significant challenge lies in ensuring that fire detection systems integrate seamlessly into our modern infrastructures. Leveraging multisensor systems can yield reliable data on potential fires. Particularly in smart buildings, the effectiveness of multisensor fire detection algorithms becomes paramount. This research introduces a fuzzy logic-driven method that harnesses the power of smoke, flame, and temperature sensors. While smoke, flames, and elevated temperatures are primary fire indicators, they can manifest concurrently or sequentially. We have amalgamated various fuzzy logic rules (IF-THEN structures) to gauge the intensity of fires. When a fire ignites, the sensors spring into action, identifying its source and promptly notifying users via the Internet and GSM modems. Moreover, they relay the fire’s precise geographic coordinates to fire departments. The fire’s status will be consistently updated on a dedicated online portal. It was observed from our results that the system proficiently sends fire alerts to residents, and the fire status undergoes regular updates at 45-second intervals. This refreshing is initiated by the identification of a designated percentage of smoke and flame. These outcomes validate the system’s effectiveness in enhancing the precision and responsiveness of fire detection capabilities.

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“…Hong et al [45] introduced FireCNN, a novel neural network model designed for active fire detection in remote sensing images. Utilizing multiscale convolution and residual acceptance, this model focuses on efficient feature extraction.…”

Section: Related Workmentioning

confidence: 99%

FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and Interpretability

Khan,

Park

2024

Electronics

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The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model’s predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies.

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Active Fire Detection Using a Novel Convolutional Neural Network Based on Himawari-8 Satellite Images

Cited by 29 publications

References 55 publications

AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network

AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and Interpretability

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