scholarly journals Improved TV Algorithm Based on Adaptive Multiplier for Interference Hyperspectral Image Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Jia Wen ◽  
Jun Wu ◽  
Fang Zhang ◽  
Ran Wei ◽  
Xianglei Xing ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Total Variation ◽  
Hyperspectral Imaging ◽  
Dictionary Learning ◽  
Hyperspectral Image ◽  
Image Decomposition ◽  
Hyperspectral Images ◽  
Data Decomposition ◽  
Imaging Spectrometer ◽  
The Ideal

Interference Hyperspectral Images (IHI) data acquired by Interference Hyperspectral Imaging Spectrometer exhibit many vertical interference stripes. The above characteristics will affect the application of dictionary learning and compressed sensing theory used on IHI data. According to the special characteristics of IHI data, many algorithms are proposed to separate the interference stripes layers and the background layers of IHI data in 2015, but the interference stripes layers are still not clean enough and the ideal background layers without interference stripes are also difficult to be obtained. In this paper, an improved total variation (TV) algorithm based on adaptive multiplier is proposed for IHI data decomposition. The value of the Lagrange multiplier is adaptive according to the unidirectional characteristics of IHI data. The proposed algorithm is used on Large Spatially Modulated Interference Spectral (LSMIS) images and is proved to provide better experimental results than the current algorithms both visually and quantitatively.

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.

Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection

2018 ◽  
Vol 34 (5) ◽  
pp. 789-798 ◽  
Author(s):  
Yuechun Zhang ◽  
Jun Sun ◽  
Junyan Li ◽  
Xiaohong Wu ◽  
Chunmei Dai
Keyword(s):  
Feature Selection ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Population Analysis ◽  
Cadmium Stress ◽  
Hyperspectral Images ◽  
Cadmium Content ◽  
Variable Combination ◽  
Non Destructive

Abstract.In order to ensure that safe and healthy tomatoes can be provided to people, a method for quantitative determination of cadmium content in tomato leaves based on hyperspectral imaging technology was put forward in this study. Tomato leaves with seven cadmium stress gradients were studied. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, then the spectral data were extracted from the hyperspectral images. To simplify the model, three algorithms of competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS) were used to select the feature wavelengths ranging from 431 to 962 nm. Final results showed that BOSS can improve prediction performance and greatly reduce features when compared with the other two selection methods. The BOSS model got the best accuracy in calibration and prediction with R2c of 0.9907 and RMSEC of 0.4257mg/kg, R2p of 0.9821, and RMSEP of 0.6461 mg/kg. Hence, the method of hyperspectral technology combined with the BOSS feature selection is feasible for detecting the cadmium content of tomato leaves, which can potentially provide a new method and thought for cadmium content detection of other crops. Keywords: Feature selection, Hyperspectral image technology, Non-destructive analysis, Regression model, Tomato leaves.

scholarly journals Current Advances in Hyperspectral Face Recognition

2020 ◽  
Author(s):  
Rizwan Qureshi ◽  
Muhammad Uzair ◽  
Anam Zahra
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Document Image ◽  
Hyperspectral Images ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
The Cost

Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and increase in the imaging speed has attracted computer vision scientists to apply hyperspectral imaging to ground based computer vision problems such as material classification, agriculture, chemistry and document image analysis. Hyperspectral imaging has also been explored for face recognition; to tackle the issues of pose and illumination variations by exploiting the richer spectral information of hyperspectral images. In this article, we present a detailed review on the potential of hyperspectral imaging for face recognition. We present hyperspectral image aquisition process and discuss key preprocessing challenges. We also discuss hyperspectral face recognition databases and techniques for feature extraction from the hyperspectral images. Potential future research directions are also highlighted

scholarly journals Current Advances in Hyperspectral Face Recognition

2020 ◽  
Author(s):  
Rizwan Qureshi ◽  
Muhammad Uzair ◽  
Anam Zahra
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Document Image ◽  
Hyperspectral Images ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
The Cost

Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and increase in the imaging speed has attracted computer vision scientists to apply hyperspectral imaging to ground based computer vision problems such as material classification, agriculture, chemistry and document image analysis. Hyperspectral imaging has also been explored for face recognition; to tackle the issues of pose and illumination variations by exploiting the richer spectral information of hyperspectral images. In this article, we present a detailed review on the potential of hyperspectral imaging for face recognition. We present hyperspectral image aquisition process and discuss key preprocessing challenges. We also discuss hyperspectral face recognition databases and techniques for feature extraction from the hyperspectral images. Potential future research directions are also highlighted

Near-infrared hyperspectral image at a glance: Some personal thoughts

NIR news ◽  
2020 ◽  
Vol 31 (5-6) ◽  
pp. 8-14
Author(s):  
José Manuel Amigo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Constructive Criticism ◽  
Constructive Discussion

First of all, I want to transmit my most humble thanks to all people who believe that I deserve the “2019 Thomas Hirschfeld” award (kindly supported by FOSS) for my work on near-infrared spectroscopy and, especially, applied on hyperspectral images. I must confess that this award caught me by surprise and that I felt a bit overwhelmed when I received it. It is an honour full of respect and responsibility. I have been given the opportunity of writing this article, and I will profit it to express different personal thoughts about general but relevant aspects of near infrared applied to hyperspectral imaging. Also, since I am more a practitioner in chemometrics (or machine learning or data mining, or …) than a developer, I will also include some insights about the beautiful combination of near-infrared hyperspectral image with chemometrics. This article is just a glimpse of constructive criticism with personal thoughts that comes from my little experience in this field. Therefore, and of course, all opinions here are open for constructive discussion with the only purpose of learning (like the machines do nowadays).

scholarly journals Determining the composition of an object based on its hyperspectral image

2021 ◽  
Vol 45 (3) ◽  
pp. 394-398
Author(s):  
A.V. Demin ◽  
E.N. Sechak ◽  
S.P. Prisyazhnyuk
Keyword(s):  
Mathematical Model ◽  
Image Analysis ◽  
Hyperspectral Imaging ◽  
Spectral Range ◽  
Hyperspectral Image ◽  
Operating Conditions ◽  
Imaging Spectrometer ◽  
Percentage Content ◽  
Hyperspectral Image Analysis ◽  
Object Based

The article presents results of the development and research of a hyperspectral imaging spectrometer for analyzing borehole fluids in real operating conditions in the spectral range from 0.35 microns to 2.1 microns. A mathematical model and an algorithm for identifying the borehole fluid by composition and percentage content based on the results of hyperspectral image analysis are developed.

scholarly journals Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering

2020 ◽  
Vol 12 (21) ◽  
pp. 3585
Author(s):  
José Prades ◽  
Gonzalo Safont ◽  
Addisson Salazar ◽  
Luis Vergara
Keyword(s):  
Independent Component Analysis ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Spectral Unmixing ◽  
Independent Component ◽  
Hyperspectral Images ◽  
Agglomerative Clustering ◽  
Number Of Clusters ◽  
Knowing How ◽  
Pixel Matching

Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.

Sparse Representation Based on Principal Component for Compressed Sensing Hyperspectral Images

2013 ◽  
Vol 321-324 ◽  
pp. 1154-1157
Author(s):  
Bin Zhou ◽  
Kun Li ◽  
Guang Xu
Keyword(s):  
Principal Component Analysis ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Information Acquisition ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Theoretical Framework ◽  
Hyperspectral Images ◽  
Compressed Sampling

Compressed sensing (CS) is a new developed theoretical framework for information acquisition and processing that breaks through the conventional Nyquist sampling limit. This paper proposes a sparse representation schemes based on principal component analysis (PCA) for CS that will be used for hyperspectral images compressed sampling. This scheme employs the prediction transform matrix to remove the correlations among successive hyperspectral measurement vectors. Experiment processes using the hyperspectral image from Earth Observing One (EO-1), and it shows a desired result both at reconstruction and denoising.

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