Semantic Segmentation of Burned Areas in Satellite Images Using a U-Net-Based Convolutional Neural Network

Brand, André; Manandhar, Achut

doi:10.5194/isprs-archives-xliii-b3-2021-47-2021

Cited by 14 publications

(10 citation statements)

References 19 publications

(15 reference statements)

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“…Until now, there have been ample studies on wildfire detection and severity mapping using remote sensing. Most of previous studies mainly used the multi-temporal method, and there have been a very few studies that try to develop a deep learning model using semantic segmentation methods [34,[85][86][87]. Our research, however, only used post-fire images to develop a deep learning-based burned area mapping algorithm using semantic segmentation methods.…”

Section: Discussionmentioning

confidence: 99%

Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach

Cho

Park

Kim

et al. 2023

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

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The risk and damage of wildfire has been increasing due to various reasons including climate change, and Republic of Korea is no exception to this situation. Burned Area Mapping is crucial not only to prevent further damage but also to manage burned areas. Burned area mapping using satellite data, however, has been limited by spatial and temporal resolution of satellite data, and classification accuracy. This paper presents a new burned area mapping method, by which damaged areas can be mapped using semantic segmentation. For this research, PlanetScope imagery that has high-resolution images with very short revisit time were used, and the proposed method is based on U-Net that requires a uni-temporal PlanetScope image. The network was trained using 17 satellite images for 12 forest fires and corresponding label images which were obtained semiautomatically by setting threshold values. Band combination tests were conducted to produce an optimal burned area mapping model. The results demonstrated that the optimal and most stable band combination is Red, Green, Blue, and NIR of PlanetScope. To improve classification accuracy, NDVI (Normalized Difference Vegetation Index), dissimilarity extracted from GLCM (Grey-Level Co-occurrence Matrix), and Land Cover Maps were used as additional datasets. In addition, topographic normalization was conducted to improve model performance and classification accuracy by reducing shadow effects. The F1 scores and overall accuracies of the final image segmentation models are ranged from 0.883 to 0.939, and from 0.990 to 0.997, respectively. These results highlight the potential of detecting burned areas using the deep learning based approach.

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Section: Discussionmentioning

confidence: 99%

Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach

Cho

Park

Kim

et al. 2023

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

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“…Other investigated classification approaches are maximum likelihood classification [57] and the Bayesian (Bayesian updating of land-cover (BULC)) algorithm [12]. Besides, different ML and deep learning (DL) approaches such as SVM [14], random forests (RFs) [53], and neural networks (NNs) [31,46,59] have been suggested. These supervised classification approaches achieve very high accuracies in mapping burned areas.…”

Section: Thermalmentioning

confidence: 99%

Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area

Florath

Keller

2022

Remote Sensing

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Bushfires pose a severe risk, among others, to humans, wildlife, and infrastructures. Rapid detection of fires is crucial for fire-extinguishing activities and rescue missions. Besides, mapping burned areas also supports evacuation and accessibility to emergency facilities. In this study, we propose a generic approach for detecting fires and burned areas based on machine learning (ML) approaches and remote sensing data. While most studies investigated either the detection of fires or mapping burned areas, we addressed and evaluated, in particular, the combined detection on three selected case study regions. Multispectral Sentinel-2 images represent the input data for the supervised ML models. First, we generated the reference data for the three target classes, burned, unburned, and fire, since no reference data were available. Second, the three regional fire datasets were preprocessed and divided into training, validation, and test subsets according to a defined schema. Furthermore, an undersampling approach ensured the balancing of the datasets. Third, seven selected supervised classification approaches were used and evaluated, including tree-based models, a self-organizing map, an artificial neural network, and a one-dimensional convolutional neural network (1D-CNN). All selected ML approaches achieved satisfying classification results. Moreover, they performed a highly accurate fire detection, while separating burned and unburned areas was slightly more challenging. The 1D-CNN and extremely randomized tree were the best-performing models with an overall accuracy score of 98 % on the test subsets. Even on an unknown test dataset, the 1D-CNN achieved high classification accuracies. This generalization is even more valuable for any use-case scenario, including the organization of fire-fighting activities or civil protection. The proposed combined detection could be extended and enhanced with crowdsourced data in further studies.

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“…The process for real-time BAS using an UAV to photograph the post-fire scene. [26] used an improved version of UNet to train and predict 258 using the burned area dataset captured by Sentinel-2. Zanetti et 259 al.…”

Section: B Burned Area Segmentation (Bas)mentioning

confidence: 99%

BASNet: Burned Area Segmentation Network for Real-Time Detection of Damage Maps in Remote Sensing Images

Liu

Fan

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Since remote sensing images of post-fire vegetation are characterized by high resolution, multiple interferences, and high similarities between the background and the target area, areas of the 2022 Beijing Winter Olympics. Fig. 1(b) shows 69 the damage map after a fire in South Korea. 70 There are many challenges for the effective retrieval of 71 burned areas from UAV images. In addition, it is important to 72 note that in the task of burned area segmentation (BAS), the 73 main purpose is to detect the whole area that has been burned 74 by fire using UAVs, which is of vital importance for post-75 disaster assessment and management. Although not belonging 76 to the burned area, new growth and unburned soil are still 77 within the scope of burned area, and thus it should be classified 78 as target burned area that is useful for further assessment 79 (The annotation of existing datasets also follows the same 80 principle). For examples, Fig. 2 shows some examples of 81 the saliency maps of different methods applied on the two 82 datasets. The first challenge is the large scale of the burned 83 area. As shown in Fig. 3(a), due to shooting time, weather 84 or geography, there are many different interferences on a 85 large area causing occlusions. Previous related studies have 86 used semantic segmentation, which in fact is a pixel-level 87 images, then longer time and more equipment are required, 146 rendering them less practical. Therefore, Tran et al. [11] used 147 cropping to process such high-resolution images with some 148 success. They cropped the images for prediction, and then 149 combined the individual results to obtain the final prediction 150 results. This resulted in two additional problems. First, the 151 burned area caused by forest fires is generally large, and the 152 segmented images may either include all burned areas or all 153 background areas in a single image. Thus, the global spatial 154 and semantic information could be lost when the deep learning 155 network performs segmentation, hence reducing the accuracy 156 of segmentation. Second, to identify large scale regions in high 157 resolution images, inputting them to the deep learning network 158 for prediction and followed by merging them require additional 159 pre-processing and post-processing. Thus, the existing BAS 160 methods cannot deal with RS images with high resolution 161 effectively. 162 In this paper, we address the challenges existing in the task 163 of segmenting post-fire burned area using RS images. The 164 major contributions of this paper are summarized as follows: 165 1) An end-to-end network BASNet based on SOD is pro-166 posed to segment the burned area using high-resolution images 167 acquired from UAV. BASNet effectively solves the problem 168 that previous methods used for BAS cannot accurately detect 169 the target in real time.170 2) The positioning and refinement modules are proposed 171 to capture the multi-level feature information by fusing rich 172 semantics with the information of spatial location and edge, 173...

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Semantic Segmentation of Burned Areas in Satellite Images Using a U-Net-Based Convolutional Neural Network

Cited by 14 publications

References 19 publications

Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach

Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach

Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area

BASNet: Burned Area Segmentation Network for Real-Time Detection of Damage Maps in Remote Sensing Images

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