Fire Tracking Based on Satellite Videos

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2	update references and layout	Amina Yu	+ 1 word(s)	1085	2022-08-26 04:17:59	\| \|
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This entry is adapted from the peer-reviewed paper 10.3390/rs14153674

Fire tracking has become an attractive application of satellite remote sensing thanks to the characteristics of recent remote sensing images, such as high frequency, large range, and multi-spectrum. Additionally, the high-resolution images provide more information and high-time resolution data in forest fire monitoring, showing great potential in environment monitoring. In recent years, many researchers have concentrated on the activate fire detection based on single images, while a few pieces of literature tracked the fire and smoke based on multi-temporal detection or continuous detection. A vital component of fire tracking from remote sensors is the accurate estimation of the background temperature of an area in a fire’s absence, which helps identify and report fire activity.

satellite video fire tracking deep learning

1. Introduction

Object tracking is a hot topic in computer vision and remote sensing, and it typically employs a bounding box that locks onto the region of interest (ROI) when only an initial state of the target (in a video frame) is available ^[1]^[2]. Thanks to the development of satellite imaging technology, various satellites with advanced onboard cameras have been launched to obtain very high resolution (VHR) satellite videos for military and civilian applications. Compared to traditional target tracking methods, satellite video target tracking is more efficient in motion analysis and object surveillance, and has shown great potential applications in spying on enemies ^[3], monitoring and protecting sea ice ^[4], fighting wildfires ^[5], and monitoring city trafficking ^[6], which traditional target tracking cannot even approach.

fire tracking methods and categorizes them into two classes, including the traditional methods and DL-based methods. A comparison of two types of fire tracking methods can be seen in Figure 1. The summary of reviewed fire tracking publications is given in Table 1.

Figure 1. Comparison diagram of fire tracking algorithm structure for the (a) traditional method and the (b) DL-based method.

Table 1. Summary of the fire tracking methods.

Target	Method	Ref.	Year	Description
Fire	Traditional	^[7]	2018	A threshold algorithm with visual interpretation
		^[8]	2019	A multi-temporal method of temperature estimation
		^[9]	2020	Temperature dynamics by data assimilation
		^[10]	2022	Wildfire tracking via visible and infrared image series
	DL-based	^[11]	2019	3D CNN to capture spatial and spectral patterns
		^[12]	2019	Inception-v3 model with transfer learning
		^[13]	2021	Near-real-time fire smoking prediction
		^[14]	2022	Combine the residual convolution and separable convolution to detect fire
		^[15]	2022	Multiple Kernel learning for various size fire detections

2. Traditional Tracking Methods

Regarding satellite imagery from satellite videos, important work for fire and smoke detection has been performed by applying the advanced Himawari imager (AHI) sensor of the Japanese geostationary weather satellite Himawari-8. The AHI offers extremely high-temporal-resolution (10 min) multispectral imagery, which is suitable for real-time wildfire monitoring on a large spatial and temporal scale.

Based on the AHI system, Ref. ^[16] investigated the feasibility of extracting real-time information about the spatial extents of wildfires. The algorithm first identified possible hotspots using the 3.9 μm and 11.2 μm bands of Himawari-8, and then eliminated false alarms by applying certain thresholds. A similar work was proposed in Ref. ^[7], which integrated a threshold algorithm and a visual interpretation method to monitor the entire process of grassland fires that occurred in the China-Mongolia border regions. To further explore the information from AHI image series, Ref. ^[8] extended their previous work and proposed a multi-temporal method of background temperature estimation. The proposed method involved a two-step process for geostationary data: a preprocessing step to aggregate the images from the AHI and a fitting step to apply a single value decomposition process for each individual pixel. Each decomposition feature map can then be compared to the raw brightness temperature data to identify thermal anomalies and track the active fire. Results showed the proposed method detected positive thermal anomalies in up to 99% of fire cases. Recently, Ref. ^[10] proposed a new object-based system for tracking the progression of individual fires via visible and infrared satellite image series. The designed system can update the attributes of each fire event in California during 2012–2020, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition.

The previous methods can overestimate the background temperature of a fire pixel and, therefore, leads to the omission of a fire event. To address this problem, Ref. ^[9] designed an algorithm that assimilated brightness temperatures from infrared images and the offset of the sunrise to the thermal sunrise time of a non-fire condition. The introduction of assimilation strategies improved the data analysis quality and computational cost, resulting in better fire detection and tracking results.

3. DL-Based Tracking Methods

Instead of exploring the fire features via manually designed operators, Ref. ^[11] investigated DL-based remote wildfire detection and tracking framework from satellite image series. They firstly processed the streaming images to purify and examined raw image data to obtain ROI. Secondly, a 3D convolutional neural network (CNN) was applied to capture spatial and spectral patterns for more accurate and robust detection. Finally, a streaming data visualization model was completed for potential wildfire incidents. The empirical evaluations highlighted that the proposed CNN models outperformed the baselines with a 94% F1 score. To improve the fire detection accuracy, authors from ^[12] developed an effective approach of a CNN based Inception-v3 with transfer learning to train the satellite images and classify the datasets into the fire and non-fire images. The confusion matrix is introduced to specify the efficiency of the proposed model, and the fire occurred region is extracted based on a local binary pattern. More recently, Ref. ^[13] explored the potential of deep learning (DL)-based fire tracking by presenting a deep fully convolutional network (FCN) to predict fire smoke, where satellite imagery in near-real-time by six bands images from the AHI sensor was used.

More DL-based methods contribute to fire detection instead of tracking. For example, Ref. ^[15] revised the general CNN models to enhance the fire detection performance in 2022. The proposed network consists of several convolution kernels with multiple sizes and dilated convolution layers with various dilation rates. Experimental results based on Landsat-8 satellite images revealed that the designed models could detect fires of varying sizes and shapes over challenging test samples, including the single fire pixels from the large fire zones. Similarly, Ref. ^[14] fused the optical and thermal modalities from the Landsat-8 images for a more effective fire representation. The proposed CNN model combined the residual convolution and separable convolution blocks to enable deeper features of the tracking target. A review of remote sensing-based fire detection is given in ^[17] in 2020, and more recent published works can be found in ^[18]^[19]^[20]. As detection is different from tracking and is out of the scope, the focus here is on tracking only and do not provide the details on fire detection. Further studies could also be conducted to extend the DL-based fire detection to DL-based fire tracking.

References

Yilmaz, A.; Javed, O.; Shah, M. Object tracking: A survey. ACM Comput. Surv. (CSUR) 2006, 38, 13.
Jiao, L.; Zhang, R.; Liu, F.; Yang, S.; Hou, B.; Li, L.; Tang, X. New Generation Deep Learning for Video Object Detection: A Survey. IEEE Trans. Neural Netw. Learn. Syst. 2021, 1–21.
Melillos, G.; Themistocleous, K.; Papadavid, G.; Agapiou, A.; Prodromou, M.; Michaelides, S.; Hadjimitsis, D.G. Integrated use of field spectroscopy and satellite remote sensing for defence and security applications in Cyprus. In Proceedings of the Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), Paphos, Cyprus, 4–8 April 2016; Volume 9688, pp. 127–135.
Xian, Y.; Petrou, Z.I.; Tian, Y.; Meier, W.N. Super-resolved fine-scale sea ice motion tracking. IEEE Trans. Geosci. Remote Sens. 2017, 55, 5427–5439.
Bailon-Ruiz, R.; Lacroix, S. Wildfire remote sensing with UAVs: A review from the autonomy point of view. In Proceedings of the 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece, 1–4 September 2020; pp. 412–420.
Du, B.; Sun, Y.; Cai, S.; Wu, C.; Du, Q. Object tracking in satellite videos by fusing the kernel correlation filter and the three-frame-difference algorithm. IEEE Geosci. Remote Sens. Lett. 2017, 15, 168–172.
Na, L.; Zhang, J.; Bao, Y.; Bao, Y.; Na, R.; Tong, S.; Si, A. Himawari-8 satellite based dynamic monitoring of grassland fire in China-Mongolia border regions. Sensors 2018, 18, 276.
Hally, B.; Wallace, L.; Reinke, K.; Jones, S.; Skidmore, A. Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data. Int. J. Digit. Earth 2019, 12, 1030–1045.
Udahemuka, G.; van Wyk, B.J.; Hamam, Y. Characterization of Background Temperature Dynamics of a Multitemporal Satellite Scene through Data Assimilation for Wildfire Detection. Remote Sens. 2020, 12, 1661.
Chen, Y.; Hantson, S.; Andela, N.; Coffield, S.R.; Graff, C.A.; Morton, D.C.; Ott, L.E.; Foufoula-Georgiou, E.; Smyth, P.; Goulden, M.L.; et al. California wildfire spread derived using VIIRS satellite observations and an object-based tracking system. Sci. Data 2022, 9, 249.
Phan, T.C.; Nguyen, T.T. Remote Sensing Meets Deep Learning: Exploiting Spatio-Temporal-Spectral Satellite Images for Early Wildfire Detection. 2019. Available online: https://Infoscience.Epfl.Ch/Record/270339 (accessed on 31 May 2022).
Vani, K. Deep learning based forest fire classification and detection in satellite images. In Proceedings of the 2019 11th International Conference on Advanced Computing (ICoAC), Chennai, India, 18–20 December 2019; pp. 61–65.
Larsen, A.; Hanigan, I.; Reich, B.J.; Qin, Y.; Cope, M.; Morgan, G.; Rappold, A.G. A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication. J. Expo. Sci. Environ. Epidemiol. 2021, 31, 170–176.
Seydi, S.T.; Saeidi, V.; Kalantar, B.; Ueda, N.; Halin, A.A. Fire-Net: A deep learning framework for active forest fire detection. J. Sens. 2022, 2022, 8044390.
Rostami, A.; Shah-Hosseini, R.; Asgari, S.; Zarei, A.; Aghdami-Nia, M.; Homayouni, S. Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning. Remote Sens. 2022, 14, 992.
Xu, G.; Zhong, X. Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8. Remote Sens. Lett. 2017, 8, 1052–1061.
Barmpoutis, P.; Papaioannou, P.; Dimitropoulos, K.; Grammalidis, N. A review on early forest fire detection systems using optical remote sensing. Sensors 2020, 20, 6442.
De Almeida Pereira, G.H.; Fusioka, A.M.; Nassu, B.T.; Minetto, R. Active fire detection in Landsat-8 imagery: A large-scale dataset and a deep-learning study. ISPRS J. Photogramm. Remote Sens. 2021, 178, 171–186.
Zhang, Q.; Ge, L.; Zhang, R.; Metternicht, G.I.; Liu, C.; Du, Z. Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection. Remote Sens. 2021, 13, 4790.
Florath, J.; Keller, S. Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area. Remote Sens. 2022, 14, 657.

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1. Introduction

2. Traditional Tracking Methods

3. DL-Based Tracking Methods

References