Chemical agent detection and identification with a hyperspectral imaging infrared sensor

2007 ◽  
Author(s):  
Vincent Farley ◽  
Martin Chamberland ◽  
Philippe Lagueux ◽  
Alexandre Vallières ◽  
André Villemaire ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Chemical Agent ◽  
Infrared Sensor ◽  
Detection And Identification

Chemical agent detection and identification with a hyperspectral imaging infrared sensor

2007 ◽  
Author(s):  
Vincent Farley ◽  
Alexandre Vallières ◽  
André Villemaire ◽  
Martin Chamberland ◽  
Philippe Lagueux ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Chemical Agent ◽  
Infrared Sensor ◽  
Detection And Identification

Chemical agent standoff detection and identification with a hyperspectral imaging infrared sensor

2009 ◽  
Author(s):  
Philippe Lagueux ◽  
Alexandre Vallières ◽  
André Villemaire ◽  
Martin Chamberland ◽  
Vincent Farley ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Chemical Agent ◽  
Infrared Sensor ◽  
Standoff Detection ◽  
Detection And Identification

Hyperspectral Imaging Infrared Sensor Used for Chemical Agent Detection and Identification

2012 ◽  
Author(s):  
Mariusz Kastek ◽  
Tadeusz Piatkowski ◽  
Rafal Dulski ◽  
Martin Chamberland ◽  
Philippe Lagueux ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Chemical Agent ◽  
Infrared Sensor ◽  
Detection And Identification

scholarly journals Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3616
Author(s):  
Jan Ubbo van Baardewijk ◽  
Sarthak Agarwal ◽  
Alex S. Cornelissen ◽  
Marloes J. A. Joosen ◽  
Jiska Kentrop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Chemical Agent ◽  
Counter Measures ◽  
Detection And Identification ◽  
Life Saving ◽  
Future Work

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

Design of a Cooperative Chemical Agent (CA) Detection Algorithm with the Hyperspectral Imaging System

2021 ◽  
Author(s):  
Hyeong-Geun Yu ◽  
Whimin Kim ◽  
Dong-Jo Park ◽  
Dong Eui Chang ◽  
Hyunwoo Nam
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Detection Algorithm ◽  
Chemical Agent

Hyperspectral imaging for the elimination of visual ambiguity in corrosion detection and identification of corrosion sources

2021 ◽  
pp. 147592172110416
Author(s):  
Dayakar N Lavadiya ◽  
Hizb Ullah Sajid ◽  
Ravi K Yellavajjala ◽  
Xin Sun
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Sulfate Solution ◽  
Vital Role ◽  
Mitigation Strategies ◽  
Svm Classifier ◽  
Corrosive Media ◽  
Detection And Identification ◽  
Coated Surfaces ◽  
Visual Ambiguity

The similarity in the hue of corroded surfaces and coated surfaces, dust, vegetation, etc. leads to visual ambiguity which is challenging to eliminate using existing image classification/segmentation techniques. Furthermore, existing methods lack the ability to identify the source of corrosion, which plays a vital role in framing the corrosion mitigation strategies. The goal of this study to employ hyperspectral imaging (1) to detect corroded surfaces under visually ambiguous scenarios and (2) identify the source of corrosion in such scenarios. To this end, three different corrosive media, namely, (1) 1M hydrochloric acid (HCl), 2) 3.5 wt.% sodium chloride solution (NaCl), and (3) 3 wt.% sodium sulfate solution (Na2SO4), are employed to generate chemically distinctive corroded surfaces. The hyperspectral imaging sensor is employed to obtain the visible and near infrared (VNIR) spectra (397 nm–1004 nm) reflected by the corroded/coated surfaces. The intensity of the reflectance in various spectral bands are considered as the descriptive features in this study, and the training and test datasets were generated consisting of 35,000 and 15,000 data points, respectively. SVM classifier is trained and then its efficacy on the test data is assessed. Furthermore, validation datasets are employed and the generalization ability of the trained SVM classifier is verified. The results from this study revealed that the SVM classifier achieved an overall accuracy of 94% with the misclassifications of 18% and 13% in the case of NaCl and Na2SO4 corrosion, respectively. Reflectance spectra obtained in the VNIR region was found to eliminate the visual ambiguity between the corroded and coated surfaces and, identify the source of corrosion accurately. Further, the range of key wavelengths of the spectra that play an important role in the distinguishability of coating and chemically distinctive corroded surface were identified to be 500–520 nm, 660–680 nm, 760–770 nm, and 830–850 nm.

Plant disease detection by hyperspectral imaging: from the lab to the field

2017 ◽  
Vol 8 (2) ◽  
pp. 238-243 ◽  
Author(s):  
A-K. Mahlein ◽  
M. T. Kuska ◽  
S. Thomas ◽  
D. Bohnenkamp ◽  
E. Alisaac ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Crop Production ◽  
Disease Incidence ◽  
Resistance Mechanism ◽  
Accurate Estimate ◽  
Plant Diseases ◽  
Negative Effects ◽  
Detection And Identification ◽  
Measuring Scale ◽  
Yield Quality

The detection and identification of plant diseases is a fundamental task in sustainable crop production. An accurate estimate of disease incidence, disease severity and negative effects on yield quality and quantity is important for precision crop production, horticulture, plant breeding or fungicide screening as well as in basic and applied plant research. Particularly hyperspectral imaging of diseased plants offers insight into processes during pathogenesis. By hyperspectral imaging and subsequent data analysis routines, it was possible to realize an early detection, identification and quantification of different relevant plant diseases. Depending on the measuring scale, even subtle processes of defence and resistance mechanism of plants could be evaluated. Within this scope, recent results from studies in barley, wheat and sugar beet and their relevant foliar diseases will be presented.

scholarly journals Hyperspectral Imaging Infrared Sensor Used for Enviromental Monitoring

2013 ◽  
Vol 124 (3) ◽  
pp. 463-467 ◽  
Author(s):  
M. Kastek ◽  
T. Piatkowski ◽  
M. Zyczkowski ◽  
M. Chamberland ◽  
P. Lagueux ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Infrared Sensor
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