scholarly journals A Low-Rate Video Approach to Hyperspectral Imaging of Dynamic Scenes

2018 ◽  
Vol 5 (1) ◽  
pp. 6 ◽  
Author(s):  
Charles Bachmann ◽  
Rehman Eon ◽  
Christopher Lapszynski ◽  
Gregory Badura ◽  
Anthony Vodacek ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Hyperspectral Imaging ◽  
Dynamic Range ◽  
Hyperspectral Image ◽  
Image Sequences ◽  
Integrated System ◽  
Imaging Systems ◽  
Spectral Bands ◽  
Spatial Dimensions ◽  
Low Rate

The increased sensitivity of modern hyperspectral line-scanning systems has led to the development of imaging systems that can acquire each line of hyperspectral pixels at very high data rates (in the 200–400 Hz range). These data acquisition rates present an opportunity to acquire full hyperspectral scenes at rapid rates, enabling the use of traditional push-broom imaging systems as low-rate video hyperspectral imaging systems. This paper provides an overview of the design of an integrated system that produces low-rate video hyperspectral image sequences by merging a hyperspectral line scanner, operating in the visible and near infra-red, with a high-speed pan-tilt system and an integrated IMU-GPS that provides system pointing. The integrated unit is operated from atop a telescopic mast, which also allows imaging of the same surface area or objects from multiple view zenith directions, useful for bi-directional reflectance data acquisition and analysis. The telescopic mast platform also enables stereo hyperspectral image acquisition, and therefore, the ability to construct a digital elevation model of the surface. Imaging near the shoreline in a coastal setting, we provide an example of hyperspectral imagery time series acquired during a field experiment in July 2017 with our integrated system, which produced hyperspectral image sequences with 371 spectral bands, spatial dimensions of 1600 × 212, and 16 bits per pixel, every 0.67 s. A second example times series acquired during a rooftop experiment conducted on the Rochester Institute of Technology campus in August 2017 illustrates a second application, moving vehicle imaging, with 371 spectral bands, 16 bit dynamic range, and 1600 × 300 spatial dimensions every second.

scholarly journals Hyperspectral Imaging for Bloodstain Identification

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3045
Author(s):  
Maheen Zulfiqar ◽  
Muhammad Ahmad ◽  
Ahmed Sohaib ◽  
Manuel Mazzara ◽  
Salvatore Distefano
Keyword(s):  
Hyperspectral Imaging ◽  
Dna Analysis ◽  
Forensic Sciences ◽  
Blood Protein ◽  
Chemical Methods ◽  
Acrylic Paint ◽  
Spectral Bands ◽  
Nail Polish ◽  
Spatial Dimensions ◽  
Non Destructive

Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.

Comparison of Evaluation Based on Different Atmospheric Correction Methods for HJ-1A Hyperspectral Imaging Data

2011 ◽  
Vol 108 ◽  
pp. 224-229 ◽  
Author(s):  
Ping Wang ◽  
Zhu Rong Xing ◽  
You Gui Feng
Keyword(s):  
Image Quality ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Atmospheric Effects ◽  
Imaging Data ◽  
Spectral Bands ◽  
Radiation Induced

HJ-1 Hyperspectral image radiometer is on the HJ-1A satellite. It will provide images about 115 spectral bands between 0.45 and 0.95μm, with spatial resolution of 100 meters and give a return visit every 96 hours. Atmospheric correction models of 6S and FLAASH were applied to the image. The results showed that both models could carry out the radiation correction to radiation-induced distortion caused by atmospheric effects, but correction results were different. 6S had well results comparing to the in-situ spectrum of vegetation. The image quality was better which had the further application ability.

scholarly journals Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 846
Author(s):  
Brian Bino Sinaice ◽  
Narihiro Owada ◽  
Mahdi Saadat ◽  
Hisatoshi Toriya ◽  
Fumiaki Inagaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dimensionality Reduction ◽  
Hyperspectral Imaging ◽  
Mining Industry ◽  
Integrated System ◽  
Classification Problems ◽  
Combined System ◽  
Rock Classification ◽  
Spectral Bands ◽  
Current Production

Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating Hyperspectral Imaging, Neighbourhood Component Analysis (NCA) and Machine Learning (ML) as a combined system that can identify rocks and minerals. Modestly put, hyperspectral imaging gathers electromagnetic signatures of the rocks in hundreds of spectral bands. However, this data suffers from what is termed the ‘dimensionality curse’, which led to our employment of NCA as a dimensionality reduction technique. NCA, in turn, highlights the most discriminant feature bands, number of which being dependent on the intended application(s) of this system. Our envisioned application is rock and mineral classification via unmanned aerial vehicle (UAV) drone technology. In this study, we performed a 204-hyperspectral to 5-band multispectral reduction, because current production drones are limited to five multispectral bands sensors. Based on these bands, we applied ML to identify and classify rocks, thereby proving our hypothesis, reducing computational costs, attaining an ML classification accuracy of 71%, and demonstrating the potential mining industry optimisations attainable through this integrated system.

scholarly journals A geophone-based and low-cost data acquisition and analysis system designed to microtremor measurements

2020 ◽  
Author(s):  
Ozkan Kafadar
Keyword(s):  
Data Acquisition ◽  
Dynamic Range ◽  
Low Cost ◽  
Internal Noise ◽  
High Gain ◽  
Integrated System ◽  
Analog To Digital ◽  
External Software ◽  
Analysis System ◽  
Microtremor Measurements

Abstract. The commercial data acquisition instruments designed for three-component microtremor measurements are usually very expensive devices. In this paper, a low-cost, computer-aided and geophone-based system designed to record, monitor and analyze the three-component microtremor data, is presented. This proposed system is not a simple data acquisition system. It is also an integrated system developed to interpret the microtermor data without any external software. Therefore, the predominant frequency and ground amplification factor of survey area can be easily estimated by using this system. The proposed system has several features such as 200 Hz sampling frequency, approximately 72 dB dynamic range, text data format and data analysis tools. This system consists of a graphical user interface developed by using .NET Framework 4.5.2 and an external hardware that includes signal conditioning circuits, voltage converter circuit, external analog-to-digital converter and Arduino Uno board. The proposed system uses the low-cost and high gain geophones with 4.5 Hz natural frequency to measure three-component microtremor data. The developed software undertakes many tasks such as communication between the external hardware and computer, transferring, monitoring and recording the seismic data to computer, and interpretation of the recorded data using the Nakamura method. To demonstrate the accuracy and precision of the proposed system, the channel consistency and internal noise measurement tests are performed. Besides, the proposed system is compared to a commercial digitizer and the obtained results are presented in this study.

scholarly journals A geophone-based and low-cost data acquisition and analysis system designed for microtremor measurements

2020 ◽  
Vol 9 (2) ◽  
pp. 365-373
Author(s):  
Ozkan Kafadar
Keyword(s):  
Data Acquisition ◽  
Dynamic Range ◽  
Low Cost ◽  
Noise Analysis ◽  
Internal Noise ◽  
Spectral Ratio ◽  
Peak Frequency ◽  
Integrated System ◽  
Open Hardware ◽  
Microtremor Measurements

Abstract. The commercial data acquisition instruments designed for three-component microtremor measurements are usually very expensive devices. In this paper, a low-cost, computer-aided, and geophone-based system designed to record, monitor, and analyze three-component microtremor data is presented. This proposed system is not a simple data acquisition system. It is also an integrated system developed to interpret the microtremor data using the horizontal-to-vertical spectral ratio (H ∕ V) method without any external software. Therefore, the H ∕ V peak frequency and amplitude can be easily estimated using this system. The proposed system has several features such as a 200 Hz sampling frequency, approximately 72 dB dynamic range, text data format, and data analysis tools. This system consists of a graphical user interface developed by using the .NET Framework 4.5.2 and external hardware that includes signal conditioning circuits, voltage converter circuit, external analog-to-digital converter, and Arduino Uno board. The proposed system uses low-cost vertical and horizontal geophones with a 4.5 Hz natural frequency to measure three-component microtremor data. The developed software undertakes many tasks such as communication between the external hardware and computer, transferring, monitoring, and recording the seismic data to the computer, and interpretation of the recorded data using the Nakamura method. Channel consistency and internal noise measurement tests were performed to demonstrate the accuracy and precision of the proposed system. The proposed system was compared to a commercial triaxial digital seismograph, and satisfactory results were obtained. The developed system is a completely open-source and open-hardware system and can be easily used in academic studies conducted by researchers and university students who are interested in seismic ambient noise analysis.

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.

scholarly journals Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2213
Author(s):  
Ahyeong Lee ◽  
Saetbyeol Park ◽  
Jinyoung Yoo ◽  
Jungsook Kang ◽  
Jongguk Lim ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Food Processing ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Uv Light ◽  
Hyperspectral Images ◽  
K Nearest Neighbor ◽  
E Coli ◽  
Discriminant Analyses

Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium) on the surface of food processing facilities by using fluorescence hyperspectral imaging. E. coli and S. typhimurium were cultured on high-density polyethylene and stainless steel coupons, which are the main materials used in food processing facilities. We obtained fluorescence hyperspectral images for the range of 420–730 nm by emitting UV light from a 365 nm UV light source. The images were used to perform discriminant analyses (linear discriminant analysis, k-nearest neighbor analysis, and partial-least squares discriminant analysis) to identify and classify coupons on which bacteria could be cultured. The discriminant performances of specificity and sensitivity for E. coli (1–4 log CFU·cm−2) and S. typhimurium (1–6 log CFU·cm−2) were over 90% for most machine learning models used, and the highest performances were generally obtained from the k-nearest neighbor (k-NN) model. The application of the learning model to the hyperspectral image confirmed that the biofilm detection was well performed. This result indicates the possibility of rapidly inspecting biofilms using fluorescence hyperspectral images.

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