DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

Seydi, Seyd Teymoor; Hasanlou, Mahdi; Chanussot, Jocelyn

doi:10.3390/rs13245138

Cited by 30 publications

(13 citation statements)

References 78 publications

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“…This study proposed the deep CNN "MultiScale-Net" for AFD using Landsat-8 imagery. The developed CNN benefited from two significant differences compared with the early DL networks: (1) The employment of convolution kernels with different sizes simultaneously in each convolution layer; (2) The utilization of dilated convolution layers with different dilation rates. The main advantages of the proposed CNN can be summarized in three aspects: I.…”

Section: Discussionmentioning

confidence: 99%

“…Previous research [25] shows that Band 7 of the Landsat-8 sensor (i.e., SWIR2) is sensitive to fire radiation. In this study, an index for AFD was proposed, namely Active Fire Index (AFI), which can be computed using Equation (1).…”

Section: Active Fire Indexmentioning

confidence: 99%

“…Fire is one of the most devastating natural hazards, causing significant damage to human property and infrastructure [1]. In recent years, forest fires have had irreversible effects around the world, such as the recent fires in Canada (2016) [2], Australia (2019) [3], and California (2020) [4].…”

Section: Introductionmentioning

confidence: 99%

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Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

et al. 2022

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Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples.

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Section: Discussionmentioning

confidence: 99%

Section: Active Fire Indexmentioning

confidence: 99%

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Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

et al. 2022

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“…More details of each step are provided in the following subsections. Deep learning models have provided promising results in many applications [6,[40][41][42][43][44][45]. Although these methods can result in a high accuracy, they are more complicated compared to conventional machine learning algorithms and require a large amount of training datasets to produce accurate results [46].…”

Section: Methodsmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping

et al. 2022

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Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model based on the Cascade Forest Model (CFM) was developed for FSM. Satellite imagery, historical reports, and field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. The performance of the proposed CFM was evaluated over two study areas, and the results were compared with those of other six machine learning methods, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Deep Neural Network (DNN), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The result showed CFM produced the highest accuracy compared to other models over both study areas. The Overall Accuracy (AC), Kappa Coefficient (KC), and Area Under the Receiver Operating Characteristic Curve (AUC) of the proposed model were more than 95%, 0.8, 0.95, respectively. Most of these models recognized the southwestern part of the Karun basin, northern and northwestern regions of the Gorganrud basin as susceptible areas.

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“…However, the highest spatial resolution of the burned-area products is 30 m, as derived from Landsat data and shown in Table 1. Research on burned-area mapping with high spatial resolution satellite data, such as Sentinel 2 of 10 m spatial resolution, has been actively pursued [6,8,15,29,30]. Stroppiana et al [15] mapped burned areas based on the Sentinel 2 data using an automatic machine learning (ML) algorithm from highly reliable fire points.…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

Gui

Zhou

et al. 2022

Remote Sensing

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The accurate detection of burned forest area is essential for post-fire management and assessment, and for quantifying carbon budgets. Therefore, it is imperative to map burned areas accurately. Currently, there are few burned-area products around the world. Researchers have mapped burned areas directly at the pixel level that is usually a mixture of burned area and other land cover types. In order to improve the burned area mapping at subpixel level, we proposed a Burned Area Subpixel Mapping (BASM) workflow to map burned areas at the subpixel level. We then applied the workflow to Sentinel 2 data sets to obtain burned area mapping at subpixel level. In this study, the information of true fire scar was provided by the Department of Emergency Management of Hunan Province, China. To validate the accuracy of the BASM workflow for detecting burned areas at the subpixel level, we applied the workflow to the Sentinel 2 image data and then compared the detected burned area at subpixel level with in situ measurements at fifteen fire-scar reference sites located in Hunan Province, China. Results show the proposed method generated successfully burned area at the subpixel level. The methods, especially the BASM-Feature Extraction Rule Based (BASM-FERB) method, could minimize misclassification and effects due to noise more effectively compared with the BASM-Random Forest (BASM-RF), BASM-Backpropagation Neural Net (BASM-BPNN), BASM-Support Vector Machine (BASM-SVM), and BASM-notra methods. We conducted a comparison study among BASM-FERB, BASM-RF, BASM-BPNN, BASM-SVM, and BASM-notra using five accuracy evaluation indices, i.e., overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), intersection over union (IoU), and Kappa coefficient (Kappa). The detection accuracy of burned area at the subpixel level by BASM-FERB’s OA, UA, IoU, and Kappa is 98.11%, 81.72%, 74.32%, and 83.98%, respectively, better than BASM-RF’s, BASM-BPNN’s, BASM-SVM’s, and BASM-notra’s, even though BASM-RF’s and BASM-notra’s average PA is higher than BASM-FERB’s, with 89.97%, 91.36%, and 89.52%, respectively. We conclude that the newly proposed BASM workflow can map burned areas at the subpixel level, providing greater accuracy in regards to the burned area for post-forest fire management and assessment.

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DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

Cited by 30 publications

References 78 publications

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

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