scholarly journals Spatial–Spectral Evidence of Glare Influence on Hyperspectral Acquisitions

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4374
Author(s):  
Alberto Signoroni ◽  
Mauro Conte ◽  
Alice Plutino ◽  
Alessandro Rizzi
Keyword(s):  
Hyperspectral Image ◽  
Imaging System ◽  
Reflectance Spectra ◽  
Hyperspectral Images ◽  
Imaging Systems ◽  
Light Sources ◽  
Optical Phenomenon ◽  
Lighting Conditions ◽  
First Time ◽  
Glare Effect

Glare is an unwanted optical phenomenon which affects imaging systems with optics. This paper presents for the first time a set of hyperspectral image (HSI) acquisitions and measurements to verify how glare affects acquired HSI data in standard conditions. We acquired two ColorCheckers (CCs) in three different lighting conditions, with different backgrounds, different exposure times, and different orientations. The reflectance spectra obtained from the imaging system have been compared to pointwise reference measures obtained with contact spectrophotometers. To assess and identify the influence of glare, we present the Glare Effect (GE) index, which compares the contrast of the grayscale patches of the CC in the hyperspectral images with the contrast of the reference spectra of the same patches. We evaluate, in both spatial and spectral domains, the amount of glare affecting every hyperspectral image in each acquisition scenario, clearly evidencing an unwanted light contribution to the reflectance spectra of each point, which increases especially for darker pixels and pixels close to light sources or bright patches.

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 Spectral Unmixing of Hyperspectral Images in the Presence of Small Targets

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Sylvain Ravel ◽  
Caroline Fossati ◽  
Salah Bourennane
Keyword(s):  
Matrix Factorization ◽  
Hyperspectral Image ◽  
Spectral Unmixing ◽  
Reflectance Spectra ◽  
Hyperspectral Images ◽  
Real World Data ◽  
Linear Combinations ◽  
Resampling Methods ◽  
Small Targets ◽  
Non Negative Matrix Factorization

Generally, the content of the hyperspectral image pixel is a mixture of the reflectance spectra of the different components in the imaged scene. In this paper, we consider a linear mixing model where the pixels are linear combinations of those reflectance spectra, called endmembers, and linear coefficients corresponding to their abundances. An important issue in hyperspectral imagery consists in unmixing those pixels to retrieve the endmembers and their corresponding abundances. We consider the unmixing issue in the presence of small targets, that is, their endmembers are only contained in few pixels of the image. We introduce a thresholding method relying on Non-negative Matrix Factorization to detect pixels containing rare endmembers. We propose two resampling methods based on bootstrap for spectral unmixing of hyperspectral images to retrieve both the dominant and rare endmembers. Our experimental results on both simulated and real world data demonstrate the efficiency of the proposed method to estimate correctly all the endmembers present in hyperspectral images, in particular the rare endmembers.

scholarly journals Large-scale hyperspectral image compression via sparse representations based on online learning

2018 ◽  
Vol 28 (1) ◽  
pp. 197-207 ◽  
Author(s):  
İrem Ülkü ◽  
Ersin Kizgut
Keyword(s):  
Large Scale ◽  
Hyperspectral Image ◽  
Optimization Algorithms ◽  
Hyperspectral Images ◽  
Learning Method ◽  
Compression Performance ◽  
Online Dictionary Learning ◽  
The One ◽  
Hyperspectral Image Compression ◽  
First Time

AbstractIn this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.

scholarly journals In-situ colour correction for digital images acquired under non-standard lighting conditions

2018 ◽  
Vol 22 (1) ◽  
pp. 42
Author(s):  
Annamalai Manickavasagan ◽  
P. M. K Alahakoon
Keyword(s):  
Computer Vision ◽  
Digital Images ◽  
Imaging System ◽  
Simple Algorithm ◽  
Light Sources ◽  
Quadratic Transformation ◽  
Under Five ◽  
Agriculture Sector ◽  
Lighting Conditions ◽  
Rgb Values

Computer vision techniques using colour images are becoming popular in food and agriculture sector. Need of a standard illumination source is an important criterion in this approach to determine various attributes based on RGB values of the objects. In general, under laboratory conditions with standard lighting, an imaging system performs with high consistency in digitizing colour. However, in field conditions where the availability of a standard light source cannot be guaranteed, the colour interpretations may not yield accurate results. The objective of this study was to develop a simple algorithm to compensate for the variations in RGB values due to varying light conditions. It is intended to be useful in situations where taking digital images of objects without standard light sources is essential for a particular purpose. A set of quadratic transformation algorithms were developed to transform the RGB values of the images acquired under five different lighting conditions. The mean variance in RGB values of the image of a colour palette (with 6 different colours) taken under five lighting conditions were in the range of 277 – 548. After implementing the developed algorithm, this was reduced to 34 – 142. Similarly, this variance was reduced from 180 – 294 to 63 – 128 in the test conducted with a plant material. This algorithm can be easily adopted in all computer vision applications where variations in colour interpretations due to nonstandard lighting sources are common. 

Research on Improved Gamma Transform Face Image Preprocessing Fusion Algorithm under Complex Lighting Conditions

2020 ◽  
Vol 13 ◽  
Author(s):  
Xiaolin Tang ◽  
Xiaogang Wang ◽  
Jin Hou ◽  
Huafeng Wu ◽  
Ping He
Keyword(s):  
Edge Detection ◽  
Face Image ◽  
Light Sources ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Image Preprocessing ◽  
Lighting Conditions ◽  
Weighted Fusion ◽  
The Face ◽  
Sobel Edge Detection

Introduction: Under complex illumination conditions such as poor light sources and light changes rapidly, there are two disadvantages of current gamma transform in preprocessing face image: one is that the parameters of transformation need to be set based on experience; the other is the details of the transformed image are not obvious enough. Objective: Improve the current gamma transform. Methods: This paper proposes a weighted fusion algorithm of adaptive gamma transform and edge feature extraction. First, this paper proposes an adaptive gamma transform algorithm for face image preprocessing, that is, the parameter of transformation generated by calculation according to the specific gray value of the input face image. Secondly, this paper uses Sobel edge detection operator to extract the edge information of the transformed image to get the edge detection image. Finally, this paper uses the adaptively transformed image and the edge detection image to obtain the final processing result through a weighted fusion algorithm. Results: The contrast of the face image after preprocessing is appropriate, and the details of the image are obvious. Conclusion: The method proposed in this paper can enhance the face image while retaining more face details, without human-computer interaction, and has lower computational complexity degree.

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

EXPRESS: Design Considerations for Discrete Frequency Infrared Microscopy Systems

2021 ◽  
pp. 000370282110133
Author(s):  
Rohit Bhargava ◽  
Yamuna Dilip Phal ◽  
Kevin Yeh
Keyword(s):  
Imaging System ◽  
Chemical Imaging ◽  
Optical Component ◽  
Imaging Systems ◽  
Imaging Data ◽  
Discrete Frequency ◽  
Figures Of Merit ◽  
Design Considerations ◽  
Hardware Implementations ◽  
Measurement Capabilities

Discrete frequency infrared (DFIR) chemical imaging is transforming the practice of microspectroscopy by enabling a diversity of instrumentation and new measurement capabilities. While a variety of hardware implementations have been realized, considerations in the design of all-IR microscopes have not yet been compiled. Here we describe the evolution of IR microscopes, provide rationales for design choices, and the major considerations for each optical component that together comprise an imaging system. We analyze design choices in illustrative examples that use these components to optimize performance, under their particular constraints. We then summarize a framework to assess the factors that determine an instrument’s performance mathematically. Finally, we summarize the design and analysis approach by enumerating performance figures of merit for spectroscopic imaging data that can be used to evaluate the capabilities of imaging systems or suitability for specific intended applications. Together, the presented concepts and examples should aid in understanding available instrument configurations, while guiding innovations in design of the next generation of IR chemical imaging spectrometers.

scholarly journals Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

2021 ◽  
Author(s):  
Annalisa Appice ◽  
Angelo Cannarile ◽  
Antonella Falini ◽  
Donato Malerba ◽  
Francesca Mazzia ◽  
...  
Keyword(s):  
Visual Attention ◽  
Hyperspectral Image ◽  
Saliency Detection ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Imaging Analysis ◽  
Analysis Task ◽  
Visual Attention Mechanism ◽  
Land Use Monitoring ◽  
Research Resources

AbstractSaliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.

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.

scholarly journals New evidence of arsenic translocation and accumulation in Pteris vittata from real-time imaging using positron-emitting 74As tracer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi Huang-Takeshi Kohda ◽  
Zhaojie Qian ◽  
Mei-Fang Chien ◽  
Keisuke Miyauchi ◽  
Ginro Endo ◽  
...  
Keyword(s):  
Imaging System ◽  
Pteris Vittata ◽  
Long Distance ◽  
Long Distance Transport ◽  
Hydroponic Cultivation ◽  
Tracer Imaging ◽  
Serial Images ◽  
First Time ◽  
New Evidence ◽  
Hyperaccumulator Plant

AbstractPteris vittata is an arsenic (As) hyperaccumulator plant that accumulates a large amount of As into fronds and rhizomes (around 16,000 mg/kg in both after 16 weeks hydroponic cultivation with 30 mg/L arsenate). However, the sequence of long-distance transport of As in this hyperaccumulator plant is unclear. In this study, we used a positron-emitting tracer imaging system (PETIS) for the first time to obtain noninvasive serial images of As behavior in living plants with positron-emitting 74As-labeled tracer. We found that As kept accumulating in rhizomes as in fronds of P. vittata, whereas As was retained in roots of a non-accumulator plant Arabidopsis thaliana. Autoradiograph results of As distribution in P. vittata showed that with low As exposure, As was predominantly accumulated in young fronds and the midrib and rachis of mature fronds. Under high As exposure, As accumulation shifted from young fronds to mature fronds, especially in the margin of pinna, which resulted in necrotic symptoms, turning the marginal color to gray and then brown. Our results indicated that the function of rhizomes in P. vittata was As accumulation and the regulation of As translocation to the mature fronds to protect the young fronds under high As exposure.

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