Hyperspectral imaging of the crime scene for detection and identification of blood stains

2013 ◽  
Author(s):  
G. J. Edelman ◽  
T. G. van Leeuwen ◽  
M. C. G. Aalders
Keyword(s):  
Hyperspectral Imaging ◽  
Crime Scene ◽  
Blood Stains ◽  
Detection And Identification

Hyperspectral imaging for the age estimation of blood stains at the crime scene

2012 ◽  
Vol 223 (1-3) ◽  
pp. 72-77 ◽  
Author(s):  
Gerda Edelman ◽  
Ton G. van Leeuwen ◽  
Maurice C.G. Aalders
Keyword(s):  
Hyperspectral Imaging ◽  
Age Estimation ◽  
Crime Scene ◽  
Blood Stains

scholarly journals Forensic analysis of beverage stains using hyperspectral imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Binu Melit Devassy ◽  
Sony George
Keyword(s):  
Hyperspectral Imaging ◽  
Crime Scene ◽  
Forensic Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Additional Information ◽  
Non Invasive ◽  
Spectral Bands ◽  
Data Dimensionality Reduction ◽  
Gradient Based

AbstractDocumentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.

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.

Visualization of Latent Blood Stains Using Visible Reflectance Hyperspectral Imaging and Chemometrics

2014 ◽  
Vol 60 ◽  
pp. S188-S192 ◽  
Author(s):  
Gerda J. Edelman ◽  
Ton G. van Leeuwen ◽  
Maurice C. Aalders
Keyword(s):  
Hyperspectral Imaging ◽  
Blood Stains ◽  
Visible Reflectance

The challenges of analysing blood stains with hyperspectral imaging

2014 ◽  
Author(s):  
J. Kuula ◽  
H.-H. Puupponen ◽  
H. Rinta ◽  
I. Pölönen
Keyword(s):  
Hyperspectral Imaging ◽  
Blood Stains

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
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