Classification Tree Analysis (Gini-Index) Smoke Detection using Himawari_8 Satellite Data Over Sumatera-Borneo Maritime Continent Sout East Asia

Ismanto, Heri; HARTONO, Hartono; Marfai, Muh Aris

doi:10.1088/1755-1315/256/1/012043

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…However, the threshold values and the features are hard to define, as they are strongly associated with the local conditions and solar zenith angles at the time of the image acquisition and vary greatly across different sensor platforms [9,10]. To further detect fire smoke pixels automatically, machine learning techniques, including traditional non-neural network techniques [13,17,18] and Multi-Layer Perceptron (MLP) neural networks [7,9], were employed using training samples extracted from visually classified polygons or by multithresholds approaches. Such approaches may have undermined the generalisability due to having few deep semantic features.…”

Section: Approaches Used In Satellite-based Fire Smoke Detectionmentioning

confidence: 99%

“…Figure 1 shows the variants of fire smoke in different scenarios captured by Landsat 8 OLI, visualised in true colour using bands 4 (red), 3 (green), and 2 (blue). Early research tried to discriminate fire smoke in the satellite imagery from other confounding objects (e.g., water, snow, cloud) based on shallow handcrafted features at the pixel level [11][12][13][14][15][16][17][18]. Such features have strong associations with various local conditions and need to be properly redefined in a different area.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model

et al. 2022

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Smoke plumes are the first things seen from space when wildfires occur. Thus, fire smoke detection is important for early fire detection. Deep Learning (DL) models have been used to detect fire smoke in satellite imagery for fire detection. However, previous DL-based research only considered lower spatial resolution sensors (e.g., Moderate-Resolution Imaging Spectroradiometer (MODIS)) and only used the visible (i.e., red, green, blue (RGB)) bands. To contribute towards solutions for early fire smoke detection, we constructed a six-band imagery dataset from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) with a 30-metre spatial resolution. The dataset consists of 1836 images in three classes, namely “Smoke”, “Clear”, and “Other_aerosol”. To prepare for potential on-board-of-small-satellite detection, we designed a lightweight Convolutional Neural Network (CNN) model named “Variant Input Bands for Smoke Detection (VIB_SD)”, which achieved competitive accuracy with the state-of-the-art model SAFA, with less than 2% of its number of parameters. We further investigated the impact of using additional Infra-Red (IR) bands on the accuracy of fire smoke detection with VIB_SD by training it with five different band combinations. The results demonstrated that adding the Near-Infra-Red (NIR) band improved prediction accuracy compared with only using the visible bands. Adding both Short-Wave Infra-Red (SWIR) bands can further improve the model performance compared with adding only one SWIR band. The case study showed that the model trained with multispectral bands could effectively detect fire smoke mixed with cloud over small geographic extents.

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Section: Approaches Used In Satellite-based Fire Smoke Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model

et al. 2022

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“…Satellite cloud images are of the Earth's cloud cover and landmark features as seen from top to bottom by meteorological satellites. One of the satellites was Himawari8, which has a visual light field of view of 0.5-1 m, a time resolution of 10 min and a spatial resolution of 500 m. The Himawari8 satellite doubles the imaging resolution and shortens the time required for global observations, which allows for capturing real-time images and accurately identifying typhoon intensity [25,26].…”

Section: Datamentioning

confidence: 99%

Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning

Jiang

Tao

2022

Atmosphere

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In this paper, a novel typhoon intensity classification and estimation network (TICAENet) is constructed to recognize typhoon intensity. The TICAENet model is based on the LeNet-5 model, which uses weight sharing to reduce the number of training parameters, and the VGG16 model, which replaces a large convolution kernel with multiple small kernels to improve feature extraction. Satellite cloud images of typhoons over the Northwest Pacific Ocean and the South China Sea from 1995–2020 are taken as samples. The results show that the classification accuracy of this model is 10.57% higher than that of the LeNet-5 model; the classification accuracy of the TICAENet model is 97.12%, with a classification precision of 97.00% for tropical storms, severe tropical storms and super typhoons. The mean absolute error (MAE) and root mean square error (RMSE) of the samples estimation in 2019 are 4.78 m/s and 6.11 m/s, and the estimation accuracy are 18.98% and 20.65% higher than that of the statistical method, respectively. Additionally, the model takes less memory and runs faster due to the weight sharing and multiple small kernels. The results show that the proposed model performs better than other methods. In general, the proposed model can be used to accurately classify typhoon intensity and estimate the maximum wind speed by extracting features from geostationary meteorological satellite images.

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“…(4) Artificial intelligence methods. The early use of traditional machine learning algorithms, such as Random Forests [29], neural networks [30], and Support Vector Machines [31], and the recent successful application of deep learning in the smoke area recognition based on video images has attracted widespread attention from scholars worldwide [12,[32][33][34][35][36]. They have tried to conduct research related to satellite image smoke area recognition based on deep learning algorithms, but it is still under exploration and development because of poor interpretability and the need for a large number of training samples.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework

Hu,

Jiang,

Qin

et al. 2024

Fire

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Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems.

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Classification Tree Analysis (Gini-Index) Smoke Detection using Himawari_8 Satellite Data Over Sumatera-Borneo Maritime Continent Sout East Asia

Cited by 6 publications

References 17 publications

Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model

Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model

Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning

An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework

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