scholarly journals The non-contact detection and identification of blood stained fingerprints using visible wavelength reflectance hyperspectral imaging: Part 1

2016 ◽  
Vol 56 (3) ◽  
pp. 181-190 ◽  
Author(s):  
Samuel Cadd ◽  
Bo Li ◽  
Peter Beveridge ◽  
William T. O’Hare ◽  
Andrew Campbell ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Contact Detection ◽  
Visible Wavelength ◽  
Detection And Identification

scholarly journals A comparison of visible wavelength reflectance hyperspectral imaging and Acid Black 1 for the detection and identification of blood stained fingerprints

2016 ◽  
Vol 56 (4) ◽  
pp. 247-255 ◽  
Author(s):  
Samuel Cadd ◽  
Bo Li ◽  
Peter Beveridge ◽  
William T. O′Hare ◽  
Andrew Campbell ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Visible Wavelength ◽  
Detection And Identification

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.

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

Hyperspectral imaging using novel LWIR OPO for hazardous material detection and identification

2014 ◽  
Author(s):  
Keith Ruxton ◽  
Gordon Robertson ◽  
Bill Miller ◽  
Graeme P. A. Malcolm ◽  
Gareth T. Maker
Keyword(s):  
Hyperspectral Imaging ◽  
Hazardous Material ◽  
Detection And Identification ◽  
Material Detection

scholarly journals A novel laboratory method for the detection and identification of cyanobacteria using hyperspectral imaging : hyperspectral imaging for cyanobacteria detection

2021 ◽  
Author(s):  
Kaytee Pokrzywinski ◽  
Cliff Morgan ◽  
Scott Bourne ◽  
Molly Reif ◽  
Kenneth Matheson ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Laboratory Method ◽  
Army Corps ◽  
Imaging Method ◽  
Accurate Identification ◽  
Active Growth ◽  
Detection And Identification ◽  
Aerial Platforms ◽  
Field Deployment ◽  
The Stability

To assist US Army Corps of Engineers resource managers in monitoring for cyanobacteria bloom events, a laboratory method using hyperspectral imaging has been developed. This method enables the rapid detection of cyanobacteria in large volumes and has the potential to be transitioned to aerial platforms for field deployment. Prior to field data collection, validation of the technology in the laboratory using monocultures was needed. This report describes the development of the detection method using hyperspectral imaging and the stability/reliability of these signatures for identification purposes. Hyperspectral signatures of different cyanobacteria were compared to evaluate spectral deviations between genera to assess the feasibility of using this imaging method in the field. Algorithms were then developed to spectrally deconvolute mixtures of cyanobacteria to determine relative abundances of each species. Last, laboratory cultures of Microcystis aeruginosa and Anabaena sp. were subjected to varying macro (nitrate and phosphate) and micro-nutrient (iron and magnesium) stressors to establish the stability of signatures within each species. Based on the findings, hyperspectral imaging can be a valuable tool for the detection and monitoring of cyanobacteria. However, it should be used with caution and only during stages of active growth for accurate identification and limited interference owing to stress.

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