scholarly journals Characterization of Background Temperature Dynamics of a Multitemporal Satellite Scene through Data Assimilation for Wildfire Detection

2020 ◽  
Vol 12 (10) ◽  
pp. 1661
Author(s):  
Gustave Udahemuka ◽  
Barend J. van Wyk ◽  
Yskandar Hamam
Keyword(s):  
Data Assimilation ◽  
False Alarm ◽  
Accurate Estimation ◽  
Temperature Cycle ◽  
Constant False Alarm Rate ◽  
Reference Dataset ◽  
High Detection Rate ◽  
Fire Event ◽  
Brightness Temperatures ◽  
Background Temperature

Detection of an active fire in an image scene relies on an accurate estimation of the background temperature of the scene, which must be compared to the observed temperature, to decide on the presence of fire. The expected background temperature of a pixel is commonly derived based on spatial-contextual information that can overestimate the background temperature of a fire pixel and therefore results in the omission of a fire event. This paper proposes a method that assimilates brightness temperatures acquired from the Geostationary Earth Orbit (GEO) sensor MSG-SEVIRI into a Diurnal Temperature Cycle (DTC) model. The expected brightness temperatures are observational forecasts derived using the ensemble forecasting approach. The threshold on the difference between the observed and expected temperatures is derived under a Constant False Alarm Rate (CFAR) framework. The detection results are assessed against a reference dataset comprised of MODIS MOD14/MYD14 and EUMETSAT FIR products, and the performance is presented in terms of user’s and producer’s accuracies, and Precision-Recall and Receiver Operating Characteristic (ROC) graphs. The method has a high detection rate when the data assimilation is implemented with an Ensemble Kalman Filter (EnKF) and a Sampling Importance Resampling (SIR) particle filter, while the weak-constraint Four-Dimensional Variational Assimilation (4D-Var) has comparatively lower detection and false alarm rates according to the reference dataset. Consideration of the diurnal variation in the background temperature enables the proposed method to detect even low-power fires.

scholarly journals An Adaptive Autogram Approach Based on a CFAR Detector for Incipient Cavitation Detection

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2303
Author(s):  
Ning Chu ◽  
Linlin Wang ◽  
Liang Yu ◽  
Changbo He ◽  
Linlin Cao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Constant False Alarm Rate ◽  
Centrifugal Pumps ◽  
High Detection Rate ◽  
Cavitation Intensity ◽  
Envelope Spectrum ◽  
Cavitation Failure

Cavitation failure often occurs in centrifugal pumps, resulting in severe harm to their performance and life-span. Nowadays, it has become crucial to detect incipient cavitation ahead of cavitation failure. However, most envelope demodulation methods suffer from strong noise and repetitive impacts. This paper proposes an adaptive Autogram approach based on the Constant False Alarm Rate (CFAR). A cyclic amplitude model (CAM) is presented to reveal the cyclostationarity and autocorrelation-periodicity of pump cavitation-caused signals. The Autogram method is improved for envelope demodulation and cyclic feature extraction by introducing the character to noise ratio (CNR) and CFAR threshold. To achieve a high detection rate, CNR parameters are introduced to represent the cavitation intensity in the combined square-envelope spectrum. To maintain a low false alarm, the CFAR detector is combined with the CNR parameter to obtain adaptive thresholds for different data along with sensor positions. By carrying out various experiments of a centrifugal water pump from Status 1 to 10 at different flow rates, the proposed approach is capable of cavitation feature extraction with respect to the CAM model, and can achieve more than a 90% detection rate of incipient cavitation and maintain a 5% false alarm rate. This paper offers an alternative solution for the predictive maintenance of pump cavitation.

A Constant False Alarm Rate-(CFAR) Based Change Detection Approach to Helicopter Diagnostics

2008 ◽  
Author(s):  
Kenneth Ranney ◽  
Hiralal Khatri ◽  
Jerry Silvious ◽  
Kwok Tom ◽  
Romeo del Rosario
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Constant False Alarm Rate ◽  
Detection Approach

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Security of Things Intrusion Detection System for Smart Healthcare

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1375
Author(s):  
Celestine Iwendi ◽  
Joseph Henry Anajemba ◽  
Cresantus Biamba ◽  
Desire Ngabo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Web Security ◽  
Intrusion Detection Systems ◽  
High Detection Rate ◽  
Detection Systems ◽  
Smart Healthcare

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

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