Spectral Matching Algorithm Research Articles

A Study on Pigment Composition of Buddhist Cave Paintings Based on Hyperspectral Technology.

The value of the Buddhist cave lies not only in the Buddha statues but also in the surface painting. Hyperspectral imaging technology, as an emerging and effective method for component identification, offers a non-contact and non-destructive approach to the preservation and restoration of oil paintings. This study employed hyperspectral cameras to capture common pigments on the surfaces of Buddhist caves. Then, the results were processed and used as a database to identify the paintings. Additionally, a series of experiments were conducted to examine the impact of binder, substrate types, and pigment sizes on the reflectance spectrum of the paints. The Spectral Angle Matching (SAM) algorithm was then used to analyze the Yuanjue Cave and Qiqushan Stone Carvings of the Tang Dynasty in China. The findings revealed that the position of absorption peaks in the reflectance spectra is not significantly influenced by the substrate but is affected by the binder. Moreover, the absorption depth varies regularly with particle size. Furthermore, the spectral matching results demonstrate that components can be accurately identified even for similar colors. Based on the pigment distribution, the study also inferred specific details of ancient paintings, including the painting steps and hidden information in the manuscript layout. These findings hold significant implications for the restoration of representative surface paintings of the Tang Dynasty Buddhist cave, providing a reference for the selection of restoration materials and methods.

Lightning graph matching

The spectral matching algorithm is a classic method for finding correspondences between two graphs, a fundamental task in pattern recognition. It has a time complexity of O(n4) and a space complexity of O(n4), where n is the number of nodes. However, such complexity limits its applicability to large-scale graph matching tasks. This paper proposes a redesign of the algorithm by transforming the graph matching problem into a one-dimensional linear assignment problem. This transformation enables efficient solving by sorting two n×1 vectors. The resulting algorithm is named the Lightning Spectral Assignment Method (LiSA), which enjoys a complexity of O(n2). Numerical experiments demonstrate the efficiency of LiSA, supported by theoretical analysis.

Rapid single-particle chemical imaging of nanoplastics by SRS microscopy.

Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 μm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 105 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.

Hyperspectral enhanced imaging analysis of nanoparticles using machine learning methods.

Nanoparticle (NP)-based technologies have gained significant attention in targeted drug delivery, encompassing chemotherapies, photodynamic therapy, and immunotherapy. Hyperspectral imaging (HSI) emerges as a label-free, minimally invasive, and high-throughput technique for quantitative NP analysis. Despite its growing importance, the application of HSI to nanoparticle analysis, especially for label-free characterization and classification, remains limited. Here, we propose a novel method integrating hyperspectral imaging with a spectral noise reduction method and machine learning (ML) for robust nanoparticle classification. There are many challenges to extracting information from noisy and overlapping particles in HSI data. To surmount these challenges, we propose a spectral angle matching (SAM) algorithm to effectively denoise hyperspectral datasets. Complementing this, we employ a support vector machine (SVM) algorithm for classification, leveraging preprocessed HSI data to extract unique spectral signatures. Our hyperspectral imaging classification of multiple nanoparticle types reveals distinct spectral characteristics inherent to each class. The classification accuracy reaches 99.9% for single nanoparticle types, highlighting the efficiency of our method. In the case of classifying multiple particle types, the overall accuracy also reaches 99.9%. Visualization of the NP classification map further demonstrates the efficacy of our model. The application of the SAM-SVM algorithm in hyperspectral analysis outperforms traditional SVM methods in classifying multiple samples, highlighting the potential of our nanoparticle analysis. Our findings not only address the challenges posed by noisy and overlapping particles but also demonstrate the potential of hyperspectral imaging in advancing real-time and label-free detection systems for diverse biomedical applications.

Towards automated molecular detection through simulated generation of CMOS-based rotational spectroscopy

The use of CMOS sensors for rotational spectroscopy is a promising, but challenging avenue for low-cost gas sensing and molecular identification. A main challenge in this approach is that practical CMOS spectroscopy samples contain various different noise sources that reduce the effectiveness of matching techniques for molecular identification with rotational spectroscopy. To help solve this challenge, we develop a software application tool that can demonstrate the feasibility and reliability of detection with CMOS sensor samples. Specifically, the tool characterizes the types of noise in CMOS sample collection and synthesizes spectroscopy files based upon existing databases of rotational spectroscopy samples gathered from other sensors. We use the software to create a large database of plausible CMOS-generated sample files of gases. This dataset is used to help evaluate spectral matching algorithms used in gas sensing and molecular identification applications. We evaluate these traditional methods on the synthesized dataset and discuss how peak finding and spectral matching algorithms can be altered to accommodate the noise sources present in CMOS sample collection.

Joint resource scheduling and trajectory design for multi-UAV-assisted uplink NOMA networks

The combination of unmanned aerial vehicle (UAV) communication and non-orthogonal multiple access (NOMA) is of great significance to support massive connections in the Internet of things (IoT). This paper studies a multi-UAV-assisted communication network, where the UAVs are employed as aerial access points to receive data from IoT nodes with NOMA adopted for uplink transmission. The aim is to maximize the average sum rate of the network, with the requirements of the individual upload of each node. To handle the node clustering problem, the spectral clustering and maximum weight matching algorithm are first integrated to divide the IoT nodes. Then, the time-varying NOMA user grouping scheme is implemented accordingly. Furthermore, by considering the trajectory decomposition and successive hover-and-fly structure, the complex UAV trajectory problem is transformed into a linear programming and the optimal time-continuous trajectory is achieved. Extensive simulations validate the effectiveness of the proposed algorithms for the UAV-assisted NOMA network.

A New Method for Calculating Water Quality Parameters by Integrating Space–Ground Hyperspectral Data and Spectral-In Situ Assay Data

The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed, which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity, and chlorophyll during data collection. The cross-correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to significantly reduce the UAV data noise. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of the multiple linear regression method (MLR), support vector machine method (SVM), and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.

Fast identification of mural pigments at Mogao Grottoes using a LIBS-based spectral matching algorithm

To quickly identify the mineral pigments in the Dunhuang murals, a spectral matching algorithm (SMA) based on four methods was combined with laser-induced breakdown spectroscopy (LIBS) for the first time. The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment. A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples, and this database was successfully used to identify unknown pigments on tablet, simulated, and real mural debris samples. The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.

A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model

ABSTRACT The spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extract water bodies using Landsat 8 Operational Land Imager (OLI) images. To compare the accuracy of the modified normalized difference water index (MNDWI), SMDPSO, and MEDPSO, we selected six areas , i.e. thermokarst lakes, Coongie Lakes National Park, the Amazon River, urban water bodies mixed with buildings, Erhai Lake that is surrounded by mountains, and high-altitude lakes. Our results show that the average overall accuracy of the MEDPSO for the six areas is 97.4%, which is higher than those of MNDWI and SMDPSO. The average commission errors and omission errors of MEDPSO (6.4% and 0.8%) are lower than those of MNDWI and SMDPSO. The MEDPSO has a higher accuracy because the maximum entropy model is a machine learning method that uses all the bands of Landsat imagery and four surface water indices in the calculation of the probability of surface water. Our study established a novel, high-precision water extraction method.

Spectral Matching Classification Method of Multi-State Similar Pigments Based on Feature Differences

The properties of the same pigments in murals are affected by different concentrations and particle diameters, which cause the shape of the spectral reflectance data curve to vary, thus influencing the outcome of matching calculations. This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences. Fast principal component analysis (FPCA) was used to calculate the eigenvalue variance of pigment spectral reflectance, then applied to the original reflectance values for parameter characterization. We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify the spectral curve. Secondly, the relative distance between the eigenvalues in the eigen variance space is combined with the JS (Jensen-Shannon) divergence to express the difference between the two spectral distributions. The JS information divergence calculates the relative distance between the eigenvalues. Experimental results show that our classification method can be used to identify the spectral curves of the same pigment under different states. The value of the root means square error (RMSE) decreased by 12.0817, while the mean values of the mean absolute percentage error (MAPE) and R2 increased by 0.0965 and 0.2849, respectively. Compared with the traditional spectral matching algorithm, the recognition error was effectively reduced.

Monitoring of Rice Irrigation Area Based on GF-1 Data and Spectral Matching Technology- A Case Study of Chuanhang Irrigation Area in Suqian City

China is the world’s largest rice producer and consumer. Accurate identification of rice planting and irrigation area is of great significance to China’s food security. Based on GF-1 multispectral data, spectral matching and OTSU algorithm were used to monitor the rice irrigation area in Chuanhang Irrigation Area from 2016 to 2020. The accuracy was verified by confusion matrix. The overall accuracy was 98.1691%, 91.8706%, 95.2451%, 93.0604%, 91.9697%, respectively. The Kappa coefficients were 0.9541, 0.7898, 0.8777, 0.8330 and 0.7824, respectively, which proved that this method was suitable for rice irrigation area extraction based on high spatial resolution remote sensing data, and could provide technical support for rice irrigation area monitoring in irrigated areas.

Airborne hyperspectral characterisation of hydrothermal alteration in a regolith-dominated terrain, southern Gawler Ranges, South Australia

Key alteration minerals associated with epithermal and porphyry Cu–Au mineralisation have been successfully identified using HyMap airborne hyperspectral imagery in a regolith-dominated terrain in the southern Gawler Ranges, South Australia. Alteration assemblages were mapped using Spectral Feature Fitting, a spectral matching algorithm, identifying the spatial distribution of localised advanced argillic and broader argillic alteration. X-ray diffraction (XRD) analysis was undertaken to independently identify the mineralogy of 57 surface soil and rock samples collected from the study area. This analysis confirmed the presence of key alteration minerals including alunite, pyrophyllite and dickite. The integration of spectral and XRD analytical techniques allowed interpretation of mineralogical patterns across the landscape. This study demonstrates that it is possible to identify surface alteration related to potential mineralisation using airborne hyperspectral imagery and semi-quantitative XRD even in a weathered regolith-dominated terrain. KEY POINTS Advanced argillic and argillic alteration identified through hyperspectral image analysis in a regolith-dominated terrain X-ray diffraction used to validate the presence of advanced argillic alteration minerals alunite, pyrophyllite and dickite in the landscape Relationship of advanced argillic and argillic alteration interpreted in two key landscape features in the context of a porphyry mineral system Demonstrates potential for detection of alteration signatures with airborne hyperspectral imagery in regolith-dominated terrains elsewhere

A Novel Spectral Matching Approach for Pigment: Spectral Subsection Identification Considering Ion Absorption Characteristics

Background: Hyperspectral technology has made it possible to perform completely non-invasive investigations on pigment analysis, in particular, on pigment identification. The most commonly used method of pigment identification is to compare the spectral similarity between ones of unknown target and ones in spectral library, which requires a comprehensive and complete spectral library and is based on overall shape of the spectrum. To a certain extent, it may ignore some of the key absorption characteristics of the spectrum. Methods: A novel spectral matching method was proposed based on the spectrum divided into subsections for identification according to the main ion absorption characteristics. Main works: (1) establishing a spectral library suitable for typical pigment identification of painting; (2) discussing the main components, as well as the absorption positions of the ions and functional groups contained in pigments frequently used by artists; (3) presenting a novel spectral matching algorithm carried on spectral subsections for pigment identification; (4) verifying the feasibility and applicability of proposed method by a Chinese painting and a fresco. Conclusions: The proposed method can correctly identify the main pigments or components contained in the mixed area, which is better than the traditional method and more convenient than the unmixing method, except for some limitations in detecting white and black pigments.

Towards automatic classification of diffuse reflectance image cubes from paintings collected with hyperspectral cameras

Knowledge of pigments and their distribution in paintings can inform conservators and scholars to better understand, and conserve these objects. In recent years, a variety of macro-scale non-contact imaging modalities have been applied to paintings to create classification/material maps. One modality, reflectance imaging spectroscopy (RIS) captures the diffuse reflectance spectral signatures of the artists’ materials present in the objects which can be used for classification (by defining regions of the image cube that have the same spectral reflectance signature). In Cultural Heritage Science the ENVI spectral hourglass wizard (ENVI-SHW) has been used to create classification maps from RIS. This method is slow and requires an expert user input during the processing in order to find the reflectance spectra that define the spectral classes (spectral endmembers or exemplars). The pigments are then deduced from spectral features in endmember spectra and from other non-invasive analytical chemical analysis of sites defined by the RIS maps. This paper explores the potential of more automated algorithms, such as maximum distance (MaxD), that are based on the theory of convex hulls for doing these classifications as well as spectral matching algorithms to an optimal spectral library. While all of these algorithms were developed for Remote Sensing community, MaxD and the library matching examine all the spectra in the reflectance image cube, require little user input, and are reproducible and fast. The results from the analysis of reflectance image cubes (400 to 950 nm) of three paintings with MaxD and a spectral library matching algorithm are presented and compared to those obtained with the ENVI-SHW algorithm. The three paintings analyzed are the central panels from illuminated leafs, two from the Laudario of Sant’Agnese (c. 1340) painted by the Master of the Dominican Effigies, namely The Nativity with the Annunciation to the Shepherds and, Christ and the Virgin Enthroned with Forty Saints, and one cutting from a leaf from a Choir book, Saint Francis Receiving the Stigmata, painted by Cosmè Tura (c. 1470s). Specifically, the results include the spectral endmembers recovered from each method as well as the associated classification maps. The findings from this study show that the MaxD algorithm was able to find the majority of the reflectance spectral endmembers with little user input.

A Sub-Neptune-sized Planet Transiting the M2.5 Dwarf G 9-40: Validation with the Habitable-zone Planet Finder

Abstract We validate the discovery of a 2-Earth-radii sub-Neptune-sized planet around the nearby high-proper-motion M2.5 dwarf G 9-40 (EPIC 212048748), using high-precision, near-infrared (NIR) radial velocity (RV) observations with the Habitable-zone Planet Finder (HPF), precision diffuser-assisted ground-based photometry with a custom narrowband photometric filter, and adaptive optics imaging. At a distance of d = 27.9 , G 9-40b is the second-closest transiting planet discovered by K2 to date. The planet’s large transit depth (∼3500 ppm), combined with the proximity and brightness of the host star at NIR wavelengths (J = 10, K = 9.2), makes G 9-40b one of the most favorable sub-Neptune-sized planets orbiting an M dwarf for transmission spectroscopy with James Webb Space Telescope, ARIEL, and the upcoming Extremely Large Telescopes. The star is relatively inactive with a rotation period of ∼29 days determined from the K2 photometry. To estimate spectroscopic stellar parameters, we describe our implementation of an empirical spectral-matching algorithm using the high-resolution NIR HPF spectra. Using this algorithm, we obtain an effective temperature of and metallicity of . Our RVs, when coupled with the orbital parameters derived from the transit photometry, exclude planet masses above 11.7M ⊕ with 99.7% confidence assuming a circular orbit. From its radius, we predict a mass of and an RV semiamplitude of , making its mass measurable with current RV facilities. We urge further RV follow-up observations to precisely measure its mass, to enable precise transmission spectroscopic measurements in the future.

A new algorithm in generating spectrum-compatible ground accelerograms

ABSTRACTArtificial ground accelerograms are usually generated to match a target response spectrum for the purpose of nonlinear dynamic analyses of structures. A simple spectral matching algorithm is proposed in this paper for engineering application. This approach directly adjusts the response deformation oneperiod at a time in order to find the modified ground accelerogram. Such a direct single-period spectral matching scheme ensures numerical stability, as compared with a multi-period spectral matching process, and has numerical efficiency, as compared with a traditional single-period spectral matching process, in generating artificial ground accelerograms. Several examples are presented to demonstrate the efficiency of this new method.

Applying hyperspectral remote sensing methods to ship detection based on airborne and ground experiments

ABSTRACT Recent advances in hyperspectral remote sensing using hundreds of spectral channels make it possible to derive oceanic variables and to understand the spatial distribution of diverse oceanic phenomena. In this study, hyperspectral measurements were obtained using airborne and ground experiments and hyperspectral data processing techniques were applied for the detection of vessels. The observed hyperspectral data were analysed by using the spectral characteristics of endmembers and a representative spectral mixture analysis technique. The pixels in the image were classified into endmembers by using spectral matching algorithms and the marine library spectra from preliminary experiments. The estimated lengths and widths of the detected ships based on a series of hyperspectral data processing methods show root mean square error (RMSE) of 2.28% and 8.72% with respect to actual ship sizes, respectively. The investigation of the errors revealed that undulating surface waves, surface water penetration of the deck, and the submerged portion of the ship below the water surface potentially contributes to unclear ship boundaries. In this study, an optimal method for the objective determination of the threshold of abundance fractions based on pixels with multiple endmember contributions is proposed. The validation results based on the proposed hyperspectral method are in good agreement with the actual sizes of the ships.

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping.

A LiDAR signature library simulated from 3-dimensional Discrete Anisotropic Radiative Transfer (DART) model to classify fuel types using spectral matching algorithms

Fuel types are one of the key variables that drive wildfire ignition and propagation. A new method is proposed to automatically classify and map fuel types from LiDAR data. The 3-dimensional Discrete Anisotropic Radiative Transfer (DART) model generated a fuel type LiDAR signature library. These simulations provided reference endmembers and additional data to demonstrate the feasibility to classify fuel types using spectral matching algorithms, like multiple endmember spectral mixture analysis (MESMA) and spectral angle mapper (SAM). When choosing a single endmember per fuel type, MESMA outperformed SAM with 63.3% and 48.9% agreement, respectively. Multiple endmembers per fuel type improved the classification results to 85.3% in SAM and 86.5% in MESMA. Endmembers need to identify different scan angles that account for the variability in height and number of trees for better results. Contrary to empirical models, a fuel type LiDAR signature library provides a comprehensive suite of solutions to classify fuel types from LiDAR data that is less study site dependent and applicable to multiple sensors.

CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification.

Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID’s performance for predicting the MS/MS spectra of certain classes of compounds, including many lipids, was quite poor. Furthermore, CFM-ID’s compound identification capabilities were limited because it did not use experimentally available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID’s performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of experimental MS/MS spectra and other metadata to enhance CFM-ID’s compound identification abilities; (3) the development of new scoring functions that improves CFM-ID’s accuracy by 21.1%; and (4) the implementation of a chemical classification algorithm that correctly classifies unknown chemicals (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server. Its source code is also accessible online.