A novel hyperspectral remote sensing tool for detecting and analyzing human materials in the environment: a geoenvironmental approach to aid in emergency response

Krekeler, Mark P.S.; Burke, Michelle; Scott, Allen J.; Sather, Barrett; Chappell, Caleb; McLeod, Claire; Loertscher, Cynthia; Loertscher, Seth; Dawson, Claudia; Brum, Jared; Fackey, Debbie

doi:10.1007/s12665-023-10761-1

Cited by 4 publications

(3 citation statements)

References 50 publications

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“…This study also opens the possibility of establishing a detailed reflective spectra and mineralogical database for makeup types on numerous substrates that can be used throughout the forensic sciences. Such a database could be integrated into existing databases [42] or hyperspectral imaging databases centered on human materials [42]. There is increasing interest in the use of hyperspectral imaging and technology for forensic purposes for materials relating to crime scenes and their investigation [50][51][52].…”

Section: Discussionmentioning

confidence: 99%

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A Reflective Spectroscopy and Mineralogical Investigation of Cosmetic Blush (Wet‘N’Wild) Potentially for Forensic Investigations Related to Interpersonal Violence—An Experimental Feasibility Study

Curtis,

Stitle,

Certain

et al. 2023

Forensic Sciences

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Interpersonal violence is a rising issue in global society and new approaches are being sought to combat the problem. Within this context, expanding forensic techniques to better document violent crime scenes is critical for improving and acquiring legal evidence, such as proving or tracing contact between victims and suspects. This project aims to demonstrate the potential for forensic investigations in the context of interpersonal violence using a field-based reflective spectroscopy approach. For this, a common cosmetic, Wet‘N’Wild “Color Icon” blush in the shade “Pearlescent Pink”, was mineralogically characterized using transmission electron microscopy and powder X-ray diffraction and subsequently investigated via reflective spectroscopy on a variety of common substrates. Differing amounts of the cosmetic product, ranging from 0.001 g to 0.075 g, were applied to a variety of substrates using a simple push method to simulate forcible contact and material transfer. Substrates included a pine wood block; (calcareous) sand from Tulum, Mexico; Ottawa sand; tile; Pergo wood; linoleum; closet material; carpets; and fabrics. The reflective spectra of cosmetic–substrate combinations were measured via an ASD FieldSpec 4 Hi-Res spectroradiometer. The Wet‘N’Wild cosmetic was reliably detected on various substrates relevant to crime scenes. Minor amounts (as low as 0.02 mg/mm2) could be detected, and average limits of detection of 0.03 mg/mm2) were achieved; however, a calcareous sand (Tulum) had a high level of detection (>0.38 mg/mm2), suggesting that further investigation is needed for more complex sand substrates. The use of the ASD spectroradiometer as a forensic tool within the context of crime scene documentation shows promise. Future work should expand the characterization of cosmetic materials across a broad range of substrates and consider systematic studies of specific population groups. Furthermore, combining this approach with hyperspectral imaging at crime scenes is a promising future direction for crime scene documentation. This work therefore demonstrates a novel method for investigating cosmetics within the context of interpersonal violence and provides a foundation for future laboratory and field work using the ASD FieldSpec 4 and hyperspectral imaging systems.

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Section: Discussionmentioning

confidence: 99%

“…Data acquisition methods for this study were adapted for this investigation from recent papers and are very similar [31,32,42,43]. Flat plastic Petri dishes were painted black and used as an experimental substrate for reflective spectra measurements.…”

Section: Spectroradiometrymentioning

confidence: 99%

A Reflective Spectroscopy and Mineralogical Investigation of Cosmetic Blush (Wet‘N’Wild) Potentially for Forensic Investigations Related to Interpersonal Violence—An Experimental Feasibility Study

Curtis,

Stitle,

Certain

et al. 2023

Forensic Sciences

Self Cite

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“…Machine learning models used on hyperspectral data have evolved over the years, starting from statistical machine learning, such as Bayesian to kernel based methods such as support vector machines, to deep learning neural networks [28][29][30][31]. There have been a few studies looking at hyperspectral spectroscopy to differentiate between species of fish, mammals, and artificial structures [32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Automated Hyperspectral Feature Selection and Classification of Wildlife Using Uncrewed Aerial Vehicles

McCraine,

Samiappan,

Kohler

et al. 2024

Remote Sensing

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Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral—rather than spatial—basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed.

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Adaptive Two-Stage Multisensor Convolutional Autoencoder Model for Lossy Compression of Hyperspectral Data

Kuester,

Gross,

Schreiner

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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A novel hyperspectral remote sensing tool for detecting and analyzing human materials in the environment: a geoenvironmental approach to aid in emergency response

Cited by 4 publications

References 50 publications

A Reflective Spectroscopy and Mineralogical Investigation of Cosmetic Blush (Wet‘N’Wild) Potentially for Forensic Investigations Related to Interpersonal Violence—An Experimental Feasibility Study

A Reflective Spectroscopy and Mineralogical Investigation of Cosmetic Blush (Wet‘N’Wild) Potentially for Forensic Investigations Related to Interpersonal Violence—An Experimental Feasibility Study

Automated Hyperspectral Feature Selection and Classification of Wildlife Using Uncrewed Aerial Vehicles

Adaptive Two-Stage Multisensor Convolutional Autoencoder Model for Lossy Compression of Hyperspectral Data

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