Multiple omics datasets reveal significant physical and physiological dormancy in alfalfa hard seeds identified by multispectral imaging analysis

Physical dormancy(PY) commonly present in the seeds of higher plants is believed to be responsible for the germination failure by impermeable seed coat in hard seeds of legume species, instead of physiological dormancy(PD). In this study, a non-destructive approach involving multispectral imaging was used to successfully identify hard seeds from non-hard seeds in Medicago sativa, with accuracy as high as96.8%–99.0%. We further adopted multiple-omics strategies to investigate the differences of physiology,metabolomics, methylomics, and transcriptomics in alfalfa hard seeds, with non-hard seeds as control.The hard seeds showed dramatically increased antioxidants and 125 metabolites of significant differences in non-targeted metabolomics analysis, which are enriched in the biosynthesis pathways of flavonoids, lipids and hormones, especially with significantly higher ABA, a hormone known to induce dormancy. In our transcriptomics results, the enrichment pathway of “response to abscisic acid” of differential expressed genes(DEG) supported the key role of ABA in metabolomics results. The methylome analysis identified 54,899, 46,216 and 54,452 differential methylation regions for contexts of CpG, CHG and CHH, and 344 DEGs might be regulated by hypermethylation and hypomethylation of promoter and exon regions, including four ABA-and JA-responsive genes. Among 8% hard seeds in seed lots,24.5% still did not germinate after scarifying seed coat, and were named as non-PY hard seeds.Compared to hard seeds, significantly higher contents of ABA/IAA and ABA/JA were identified in nonPY hard seeds, which indicated the potential presence of PD. In summary, the significantly changed metabolites, gene expressions, and methylations all suggested involvement of ABA responses in hard seeds, and germination failure of alfalfa hard seeds was caused by combinational dormancy(PY + PD),rather than PY alone.

Analysis of rice paper's morphological features based on multispectral imaging technology

Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice paper is an important carrier of traditional Chinese painting arts. In this paper, rice paper＇s morphological feature analysis is done using multi spectral imaging technology. The multispectral imaging system is utilized to acquire rice paper＇s spectral images in different wave- length channels, and then those spectral images are measured using texture parameter statistics to acquire sensitive bands for rice paper＇s feature. The mathematical morphology and grayscale statistical principle are utilized to establish a rice paper＇s morphological feature analytical model which is used to acquire rice paper＇ s one-dimensional vector. For the purpose of eval- uating these feature vectors＇ accuracy, they are entered into the support vector machine（SVM） classifier for detection and classification. The results show that the rice paper＇s feature is out loud in the spectral band 550 nm, and the average classifi- cation accuracy of feature vectors output from the analytical model is 96 %. The results indicate that the rice paper＇s feature analytical model can extract most of rice paper＇s features with accuracy and efficiency.

Multispectral imaging of acute wound tissue oxygenation

This paper investigates the appropriate range of values for the transcutaneous blood oxygen saturation(StO2)of granulating tissues and the surrounding tissue that can ensure timely wound recovery.This work has used a multispectral imaging system to collect wound images at wave-lengths ranging between 520 nm and 600 nm with a resolution of 10 nm.As part of this research,a pilot study was conducted on three injured individuals with superfcial wounds of different wound ages at different skin locations.The S_(t)O_(2)value predicted for the examined wounds using the Extended Modified Lambert-Beer model revealed a mean S_(t)O_(2)of 61±10.3%compared to 41.6±6.2%at the surrounding tissues,and 50.1±1.53%for control sites.These preliminary results contribute to the existing knowledge on the possible range and variation of wound bed S_(t)O_(2)that are to be used as indicators of the functioning of the vasomotion system and wound health.This study has concluded that a high S_(t)O_(2)of approximately 60%and a large fuctuation in this value should precede a good progression in wound healing.

Feature spectrum extraction of human fingernails based on LCTF multispectral imaging

A multispectral imaging system consisting of liquid crystal tunable filter(LCTF)and charge coupled device(CCD)camera was used to collect the images of fingernail samples at intervals of 10 nm during the spectral range of 450-1 000 nm,and a multispectral image of human fingernails containing 56 bands was obtained.The accurate reflectivity information of fingernails was obtained through referring whiteboard comparative measurement method.Principal component analysis(PCA)and band index method were used to reduce the dimension of the sample images respectively and two feature spaces were obtained.Spectral angle mapping(SAM)was used to classify human fingernails in these two feature spaces.The classification accuracy were above 92.5%and 82.9%respectively.Therefore,the feature space obtained by the PCA can be used as the characteristic spectrum of human fingernails,which provides a reliable basis for the analysis of multispectral spectrum of fingernails and human health assessment in the future.

External defects and severity level evaluation of potato using single and multispectral imaging in near infrared region Author links open overlay panel

Non-invasive potato defects detection has been demanded for sorting and grading purpose.Researches on the classification of the defects has been available,however,investigation on the severity level calculation is limited.For the detection of the common scab,it has been found that imaging in the infrared region provide an interesting characteristic that could distinguish defected area to normal area.Thus,investigations on this wavelength range is interesting to add more knowledge and for applications.In this research,the multispectral image has been obtained and investigated especially at three wavelengths(950,1150,1600 nm).Image pre-processing and pseudo-color conversion techniques were explored to enhance the contrast between defects,normal background skin area and soil deposits.Results show that external defects,such as common scab and some mechanical damage types,appear brighter in the near infrared region,especially at 1600 nm against the normal skin background.It has been found that pseudo-color images conversion provides more information regarding type if surface characteristics compared to grayscale single imaging.Image segmentation using pseudo-color images after multiplication operation pre-processing could be used for common scab and mechanical damage detection excluding soil deposits with a Dice Sorensen coefficient of 0.64.In addition,image segmentation using single image at 1600 nm shown relatively better results with Dice Sorensen coefficient of 0.72 with note that thick soil deposits will also be segmented.Defect severity level evaluation had an R2 correlation of 0.84 against standard measurements of severity.

Multispectral imaging systems for airborne remote sensing to support agricultural production management

This study investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production.The three systems have been used in agricultural studies.They range from low-cost to relatively high-cost,manually operated to automated,multispectral composite imaging with a single camera and integrated imaging with custom-mounting of separate cameras.Practical issues regarding use of the imaging systems were described and discussed.The low-cost system,due to band saturation,slow imaging speed and poor image quality,is more preferable to slower moving platforms that can fly close to the ground,such as unmanned autonomous helicopters,but not recommended for low or high altitude aerial remote sensing on fixed-wing aircraft.With the restriction on payload unmanned autonomous helicopters are not recommended for high-cost systems because they are typically heavy and difficult to mount.The system with intermediate cost works well for low altitude aerial remote sensing on fixed-wing aircraft with field shapefile-based global positioning triggering.This system also works well for high altitude aerial remote sensing on fixed-wing aircraft with global positioning triggering or manually operated.The custom-built system is recommended for high altitude aerial remote sensing on fixed-wing aircraft with waypoint global positioning triggering or manually operated.

All-inorganic lead-free NiO_(x)/Cs_(3)Bi_(2)Br_(9) perovskite heterojunction photodetectors for ultraviolet multispectral imaging

Bismuth-based perovskites are considered to be promising candidates to substitute the toxic lead-based perovskite in optoelectronics due to their excellent optoelectronic properties,high environmental friendliness,and(moisture,light,and heat)stability.However,there are still few reports about high performance bismuth-based perovskite ultraviolet photodetectors,and is more lacking in ultraviolet imaging demonstration.Herein,we reported a self-powered NiO_(x)/Cs_(3)Bi_(2)Br_(9) heterojunction photodetector with excellent photodetection performance by electrochemical depositing NiOx as the hole transport layer.The optimized NiO_(x)/CsaBi_(2)Brg heterojunction photodetector exhibits excellent ultraviolet detection performance with a fast response speed of 3.04/4.65 ms,wide linear dynamic range of 116.6 dB,decent responsivity of 4.33 mA·W^(-1) at 0 V bias,and high detectivity of 1.3×10^(11) jones.The outstanding performance of the optimized NiO_(x)/Cs_(3)Bi_(2)Br_(9) heterojunction photodetector is enough to meet the high-quality ultraviolet imaging.Therefore,we further integrated the optimized NiO_(x)/Cs_(3)Bi_(2)Br_(9) heterojunction photodetector to the transmission mode ultraviolet multispectral imaging system,achieving admirable imaging results at weak light condition.This work will play a positive role in promoting the development of bismuth-based ultraviolet photodetection and ultraviolet multispectral imaging.

Applications of multiplexed immunohistochemistry/immunofluorescence and multispectral imaging technology in the field of tumor immunotherapy

Multiplexed immunohistochemistry/fluorescence(mIHC/IF)in combination with multispectral unmixing is a novel multitarget histopathological staining and imaging technique.By simultaneously revealing expression level and spatial information for up to eight biomarkers in situ,in addition to a nuclear stain within a single formalin-fixed paraffin-embedded(FFPE)tissue section,this technology can analyze the phenotype,abundance,morphology and intercellular relationship of cells while providing statistically significant results.In recent years,technical improvements have brought new insight into mIHC/IF and multispectral imaging approaches,which have been successfully applied in the field of cancer immunotherapy,specifically in regard to tumor microenvironment research,immunotherapy drug discovery,and prognostic and metastatic risk evaluation.This review highlights the principle,workflow,advantages and disadvantages of the technology,and discusses the latest applications of mIHC/IF-based imaging technology in the field of TME-related research and immunotherapy drug discovery.

Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

End-to-end computational design for an EUV solar corona multispectral imager with stray light suppression

An extreme ultraviolet solar corona multispectral imager can allow direct observation of high temperature coronal plasma,which is related to solar flares,coronal mass ejections and other significant coronal activities.This manuscript proposes a novel end-to-end computational design method for an extreme ultraviolet(EUV)solar corona multispectral imager operating at wavelengths near 100 nm,including a stray light suppression design and computational image recovery.To suppress the strong stray light from the solar disk,an outer opto-mechanical structure is designed to protect the imaging component of the system.Considering the low reflectivity(less than 70%)and strong-scattering(roughness)of existing extreme ultraviolet optical elements,the imaging component comprises only a primary mirror and a curved grating.A Lyot aperture is used to further suppress any residual stray light.Finally,a deep learning computational imaging method is used to correct the individual multi-wavelength images from the original recorded multi-slit data.In results and data,this can achieve a far-field angular resolution below 7",and spectral resolution below 0.05 nm.The field of view is±3 R_(☉)along the multi-slit moving direction,where R☉represents the radius of the solar disk.The ratio of the corona's stray light intensity to the solar center's irradiation intensity is less than 10-6 at the circle of 1.3 R_(☉).

Feasibility Study for Applying Spectral Imaging for Wheat Grain Authenticity Testing in Pasta

Authentication of pasta is currently determined using molecular biology-based techniques focusing on DNA as the target analyte. Whilst proven to be effective, these approaches can be criticised as being destructive, time consuming, and requiring specialist instrument training. Advances in the field of multispectral imaging (MSI) and hyperspectral imaging (HSI) have facilitated the development of compact imaging platforms with the capability to rapidly differentiate a range of materials (inclusive of grains and seeds) based on surface colour, texture and chemical composition. This preliminary investigation evaluated the applicability of spectral imaging for identification and quantitation of durum wheat grain samples in relation to pasta authenticity. MSI and HSI were capable of rapidly distinguishing between durum wheat and adulterant common wheat cultivars and assigning percentage adulteration levels characterised by low biases and good repeatability estimates. The results demonstrated the potential for spectral imaging based seed/grain adulteration testing to augment existing standard molecular approaches for food authenticity testing.

Multichannel imaging for monitoring chemical composition and germination capacity of cowpea(Vigna unguiculata) seeds during development and maturation

This study aimed to set a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The changes in germination capacity and variations in moisture, protein and different sugars during twelve stages of seed development from 10 to 32 days after anthesis were nondestructively monitored. Multispectral data at 20 discrete wavelengths in the ultraviolet, visible and near infrared regions were extracted from individual seeds and then modelled using partial least squares regression and linear discriminant analysis(LDA) models. The developed multivariate models were accurate enough for monitoring all possible changes occurred in moisture, protein and sugar contents with coefficients of determination in prediction R^(2) of 0.93, 0.80 and 0.78 and root mean square errors in prediction(RMSEP) of 6.045%, 2.236% and 0.890%, respectively. The accuracy of PLS models in predicting individual sugars such as verbascose and stachyose was reasonable with R~2 of 0.87 and 0.87 and RMSEP of 0.071%and 0.485%, respectively;but for the prediction of sucrose and raffinose the accuracy was relatively limited with R^(2) of 0.24 and 0.66 and RMSEP of 0.567% and 0.045%, respectively. The developed LDA model was robust in classifying the seeds based on their germination capacity with overall correct classification of96.33% and 95.67% in the training and validation datasets, respectively. With these levels of accuracy,the proposed multichannel spectral imaging system designed for single seeds could be an effective choice as a rapid screening and non-destructive technique for identifying the ideal harvesting time of cowpea seeds based on their chemical composition and germination capacity. Moreover, the development of chemical images of the major constituents along with classification images confirmed the usefulness of the proposed technique as a non-destructive tool for estimating the concentrations and spatial distributions of moisture, protein and sugars during different developmental stages of cowpea seeds.

ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture imagesof the fundus by illuminating it with different wavelengths of light. However,these images are taken at different points in time such that eyeball movementscan cause misalignment between consecutive images. The multispectral imagesequence reveals important information in the form of retinal and choroidal bloodvessel maps, which can help ophthalmologists to analyze the morphology of theseblood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deeplearning framework called “Adversarial Segmentation and Registration Nets”(ASRNet) for the simultaneous estimation of the blood vessel segmentation andthe registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills theblood vessel segmentation task, and (ii) A registration module R that estimatesthe spatial correspondence of an image pair. Based on the segmention-drivenregistration network, we train the segmentation network using a semi-supervisedadversarial learning strategy. Our experimental results show that the proposedASRNet can achieve state-of-the-art accuracy in segmentation and registrationtasks performed with real MSI datasets.

Characterization of a Multimodal and Multispectral Led Imager: Application to Organic Polymer’s Microspheres with Diameter Φ = 10.2 μm

Multispectral microscopy enables information enhancement in the study of specimens because of the large spectral band used in this technique. A low cost multimode multispectral microscope using a camera and a set of quasi-monochromatic Light Emitting Diodes (LEDs) ranging from ultraviolet to near-infrared wavelengths as illumination sources was constructed. But the use of a large spectral band provided by non-monochromatic sources induces variation of focal plan of the imager due to chromatic aberration which rises up the diffraction effects and blurs the images causing shadow around them. It results in discrepancies between standard spectra and extracted spectra with microscope. So we need to calibrate that instrument to be a standard one. We proceed with two types of images comparison to choose the reference wavelength for image acquisition where diffraction effect is more reduced. At each wavelength chosen as a reference, one image is well contrasted. First, we compare the thirteen well contrasted images to identify that presenting more reduced shadow. In second time, we determine the mean of the shadow size over the images from each set. The correction of the discrepancies required measurements on filters using a standard spectrometer and the microscope in transmission mode and reflection mode. To evaluate the capacity of our device to transmit information in frequency domain, its modulation transfer function is evaluated. Multivariate analysis is used to test its capacity to recognize properties of well-known sample. The wavelength 700 nm was chosen to be the reference for the image acquisition, because at this wavelength the images are well contrasted. The measurement made on the filters suggested correction coefficients in transmission mode and reflection mode. The experimental instrument recognized the microsphere’s properties and led to the extraction of the standard transmittance and reflectance spectra. Therefore, this microscope is used as a conventional instrument.

MULTISPECTRAL UNMIXING OF FLUORESCENCE MOLECULAR TOMOGRAPHY DATA

Even though multispectral imaging is considered very significant in biological imaging,it is only commonly used in microscopy in a 2D approach.Here,we present a Fluorescence Molecular Tomography system capable of recording simultaneously tomographic data at several spectral windows,enabling multispectral tomography.3D reconstructed data from several spectral windows is used to construct a linear unmixing algorithm for multispectral deconvolution of overlapping fluorescence signals.The method is applied on tomographic 3D fluorescence concentration maps in tissue-mimicking phantoms,yielding absolute quantification of the concentration of each individual fluorophore.Results are compared to the case when unmixing is performed in the raw 2D data instead of the reconstructed 3D concentration map,showing greater accuracy when unmixing algorithms are applied in the reconstructed data.Both the reflection and transmission geometries are considered.

Fusion of multispectral image and panchromatic image based on NSCT and NMF

A novel fusion method of multispectral image and panchromatic image based on nonsubsampled contourlet transform（NSCT） and non-negative matrix factorization（NMF） is presented,the aim of which is to preserve both spectral and spatial information simultaneously in fused image.NMF is a matrix factorization method,which can extract the local feature by choosing suitable dimension of the feature subspace.Firstly the multispectral image was represented in intensity hue saturation（IHS） system.Then the I component and panchromatic image were decomposed by NSCT.Next we used NMF to learn the feature of both multispectral and panchromatic images＇ low-frequency subbands,and the selection principle of the other coefficients was absolute maximum criterion.Finally the new coefficients were reconstructed to get the fused image.Experiments are carried out and the results are compared with some other methods,which show that the new method performs better in improving the spatial resolution and preserving the feature information than the other existing relative methods.

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image.The classification accuracy is mainly influenced by the feature sets that are selected to classify the image.In the past,handcrafted feature sets are used which are not adaptive for different image domains.To overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose.For extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions.For selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based technique.Fivefold Multiclass Support Vector Machine(MSVM)is used for classification purpose.Experiments con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

D-SS Frame:deep spectral-spatial feature extraction and fusion for classification of panchromatic and multispectral images

Facing the very high-resolution( VHR) image classification problem,a feature extraction and fusion framework is presented for VHR panchromatic and multispectral image classification based on deep learning techniques. The proposed approach combines spectral and spatial information based on the fusion of features extracted from panchromatic( PAN) and multispectral( MS) images using sparse autoencoder and its deep version. There are three steps in the proposed method,the first one is to extract spatial information of PAN image,and the second one is to describe spectral information of MS image. Finally,in the third step,the features obtained from PAN and MS images are concatenated directly as a simple fusion feature. The classification is performed using the support vector machine( SVM) and the experiments carried out on two datasets with very high spatial resolution. MS and PAN images from WorldView-2 satellite indicate that the classifier provides an efficient solution and demonstrate that the fusion of the features extracted by deep learning techniques from PAN and MS images performs better than that when these techniques are used separately. In addition,this framework shows that deep learning models can extract and fuse spatial and spectral information greatly,and have huge potential to achieve higher accuracy for classification of multispectral and panchromatic images.

A rate control approach to distributed source coding for interferential multispectral image compression

Distributed source coding （DSC） is applied to interferential multispectral image compression owing to strong correlation among the image frames. Many DSC systems in the literature use feedback channel （FC） to control rate at the decoder, which limits the application of DSC. Upon an analysis of the image data, a rate control approach is proposed to avoid FC. Low-complexity motion compensation is applied first to estimate side information at the encoder. Using a polynomial fitting method, a new mathematical model is then derived to estimate rate based on the correlation between the source and side information. The experimental results show that our estimated rate is a good approximation to the actual rate required by FC while incurring a little bit-rate overhead. Our compression scheme performs comparable with the FC based DSC system and outperforms JPEG2000 significantly.

An image compression method for space multispectral time delay and integration charge coupled device camera

Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems （CCSDS） has proposed an image data compression （CCSDS-IDC） algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in mukispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC （ICCSDS-IDC）-based distributed source coding （DSC） scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian-Wolf （SW） coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band.