scholarly journals Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2125
Author(s):  
Bardia Yousefi ◽  
Clemente Ibarra-Castanedo ◽  
Martin Chamberland ◽  
Xavier P. V. Maldague ◽  
Georges Beaudoin
Keyword(s):  
Matched Filter ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Clustering Methods ◽  
Spectral Angle Mapper ◽  
Long Wave ◽  
Clustering Approach ◽  
Spectral Comparison ◽  
Computational Simplicity ◽  
Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

scholarly journals Application of Gaofen-5 Hyperspectral Data in Uranium Exploration: A Case Study of Weijing in Inner Mongolia, China

2021 ◽  
Author(s):  
Yuantao Zhang ◽  
Wei Pan ◽  
Zhangfa Yu
Keyword(s):  
Inner Mongolia ◽  
Matched Filter ◽  
Principal Component ◽  
Uranium Mineralization ◽  
Hyperspectral Data ◽  
Full Spectrum ◽  
Uranium Exploration ◽  
Geological Information ◽  
Texture Complexity ◽  
Occurrence Matrix

Abstract Gaofen-5 (GF-5) satellite is the world's first full-spectrum hyperspectral satellite to achieve comprehensive observations of the atmosphere and land. The Advanced Hyperspectral Imager (AHSI) carried by GF-5 can acquire 330-chanel imagery covering 390 - 2500 nm. However, the application of GF-5 AHSI imagery in uranium exploration is currently unknown. In this paper, the AHSI imagery was used for prospecting uranium mineralization in the Weijing, Inner Mongolia, China. The matched filter (MF) and threshold segmentation were used for mapping goethite, Al-high, Al-medium and Al-poor sericite. And the principal component analysis (PCA) and gray-level co-occurrence matrix (GLCM) were used to extract the texture information of the study area. Subsequently, combined with geological information, the relationship between alteration information, texture complexity and uranium mineralization was discussed, and it was pointed out that goethite, Al-medium, Al-poor sericite and texture complexity in this area can be used as indicators of uranium mineralization. Finally, three prospects were delineated, which will guide the follow-up uranium exploration in this area and promote the application of GF-5 AHSI data.

scholarly journals Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

2014 ◽  
Vol XL-8 ◽  
pp. 667-670 ◽  
Author(s):  
R. Vidhya ◽  
D. Vijayasekaran ◽  
M. Ahamed Farook ◽  
S. Jai ◽  
M. Rohini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Status ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Spectral Angle Mapper ◽  
Svm Classification ◽  
Sensing Data

Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.

scholarly journals Time lapse synchrotron IR chemical imaging for observing the acclimation of a single algal cell to CO2 treatment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ghazal Azarfar ◽  
Ebrahim Aboualizadeh ◽  
Simona Ratti ◽  
Camilla Olivieri ◽  
Alessandra Norici ◽  
...  
Keyword(s):  
Algal Cell ◽  
Principal Component ◽  
External Perturbation ◽  
Time Lapse ◽  
Chemical Imaging ◽  
Hyperspectral Data ◽  
Aqueous Environment ◽  
Ir Absorption ◽  
Imaging Data ◽  
Mid Infrared

AbstractAlgae are the main primary producers in aquatic environments and therefore of fundamental importance for the global ecosystem. Mid-infrared (IR) microspectroscopy is a non-invasive tool that allows in principle studying chemical composition on a single-cell level. For a long time, however, mid-infrared (IR) imaging of living algal cells in an aqueous environment has been a challenge due to the strong IR absorption of water. In this study, we employed multi-beam synchrotron radiation to measure time-resolved IR hyperspectral images of individual Thalassiosira weissflogii cells in water in the course of acclimation to an abrupt change of CO2 availability (from 390 to 5000 ppm and vice versa) over 75 min. We used a previously developed algorithm to correct sinusoidal interference fringes from IR hyperspectral imaging data. After preprocessing and fringe correction of the hyperspectral data, principal component analysis (PCA) was performed to assess the spatial distribution of organic pools within the algal cells. Through the analysis of 200,000 spectra, we were able to identify compositional modifications associated with CO2 treatment. PCA revealed changes in the carbohydrate pool (1200–950 cm$$^{-1}$$ - 1 ), lipids (1740, 2852, 2922 cm$$^{-1}$$ - 1 ), and nucleic acid (1160 and 1201 cm$$^{-1}$$ - 1 ) as the major response of exposure to elevated CO2 concentrations. Our results show a local metabolism response to this external perturbation.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid Line to Line Analysis Tool

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4436
Author(s):  
Mohammad Al Ktash ◽  
Mona Stefanakis ◽  
Barbara Boldrini ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Solid Phase ◽  
Ultra Violet ◽  
Principal Component ◽  
Ccd Camera ◽  
Conveyor Belt ◽  
Hyperspectral Data ◽  
Analysis Tool ◽  
Sample Set

A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.

scholarly journals Spatial Conditions Supporting Sustainable Development of Enterprises on Local Level

2021 ◽  
Vol 13 (4) ◽  
pp. 2292
Author(s):  
Aneta Ptak-Chmielewska ◽  
Agnieszka Chłoń-Domińczak
Keyword(s):  
Social Insurance ◽  
Small And Medium Enterprises ◽  
Local Level ◽  
Principal Component ◽  
Sources Of Information ◽  
Clustering Methods ◽  
Significant Information ◽  
The Status ◽  
Medium Enterprises ◽  
High Share

Micro, small and medium enterprises (MSMEs) represent more than 99% of enterprises in Europe. Therefore, knowledge about this sector, also in the spatial context is important to understand the patterns of economic and social development. The main goal of this article is an analysis of spatial conditions and the situation of MSMEs on a local level using combined sources of information. This includes data collected in the Social Insurance Institution and Tax registers in Poland, which provides information on the employment, wages, revenues and taxes paid by the MSMEs on a local level as well as contextual statistical information. The data is used for a diagnosis of spatial circumstances and discussion of conditions influencing the status of the MSMEs sector in a selected region (voivodeship) in Poland. Taxonomy methods including factor analysis and clustering methods based on k-means and SOM Kohonen were used for selecting significant information and grouping of the local units according to the situation of the MSMEs. There are eight factors revealed in principal component analysis and five clusters of local units distinguished using these factors. These include two clusters with a high share of rural local units and two clusters with a high share of rural-urban and urban local units. Additionally, there was an outstanding cluster with only two dominant urban local units. Factors show differences between clusters in the situation of MSMEs sector and infrastructure. Different spatial conditions in different regions influence the situation of MSMEs.

scholarly journals Hyperspectral monitoring of fructose content in marzipan

2018 ◽  
Vol 14 (s1) ◽  
pp. 79-88
Author(s):  
Katalin Badak-Kerti ◽  
Szabina Németh ◽  
Andreas Zitek ◽  
Ferenc Firtha
Keyword(s):  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Spectral Properties ◽  
Oil Content ◽  
Sugar Content ◽  
Principal Component ◽  
Reducing Sugar ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Analysis Technique

In our research marzipan samples of different sugar to almond paste ratios (1:1, 2:1, 3:1) were stored at 17 °C. Reducing sugar content was measured by analytical method, texture analysis was done by penetrometry, electric characteristics were measured by conductometry and hyperspectral images were taken 6–8 times during the 16 days of storage. For statistical analyses (discriminant analysis, principal component analysis) SPSS program was used. According to our findings with the hyperspectral analysis technique, it is possible to identify how long the samples were stored (after production), and to which class (ratio of sugar to almond) the sample belonged. The main wavelengths which gave the best discrimination results among the days of storage were between 960 and 1100 nm. The type of the marzipan was easy to distinguish with the hyperspectral data; the biggest differences were observed at 1200 and 1400 nm, which are connected to the first overtone of C-H bound, therefore correlate with the oil content. The spatial distribution of penetrometric, electric and spectral properties were also characteristic to fructose content. The fructose content of marzipan is difficult to measure by usual optical ways (polarimetry, spectroscopy), but since fructose is hygroscopic, the spatial distribution of spectral properties can be characteristic.

scholarly journals MINERALOGICAL MAPPING IN THE PART OF A GOLD PROSPECT USING EO-1 HYPERION DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 991-993 ◽  
Author(s):  
V. K. Sengar ◽  
A. S. Venkatesh ◽  
P. K. Champaty Ray ◽  
S. L. Chattoraj ◽  
R. U. Sharma
Keyword(s):  
Hyperspectral Data ◽  
Reflectance Spectra ◽  
High Spectral Resolution ◽  
Mineral Potential Mapping ◽  
Spectral Angle Mapper ◽  
Potential Mapping ◽  
Hyperion Data ◽  
End Members ◽  
Resolution Of Imaging ◽  
Hyperion Image

The satellite data obtained from various airborne as well as space-borne Hyperspectral sensors, often termed as imaging spectrometers, have great potential to map the mineral abundant regions. Narrow contiguous bands with high spectral resolution of imaging spectrometers provide continuous reflectance spectra for different Earth surface materials. Detailed analysis of resultant reflectance spectra, derived through processing of hyperspectral data, helps in identification of minerals on the basis of their reflectance characteristics. EO-1 Hyperion sensor contains 196 unique channels out of 242 bands (L1R product) covering 0.4&amp;ndash;2.5&amp;thinsp;μm range has also been proved significant in the field of spaceborne mineral potential mapping. <br><br> Present study involves the processing of EO-1 Hyperion image to extract the mineral end members for a part of a gold prospect region. Mineral map has been generated using spectral angle mapper (SAM) method of image classification while spectral matching has been done using spectral analyst tool in ENVI. Resultant end members found in this study belong to the group of minerals constituting the rocks serving as host for the gold mineralisation in the study area.

scholarly journals Hyperspectral data compression based upon the principal component analysis

2021 ◽  
Vol 45 (2) ◽  
pp. 235-244
Author(s):  
A.S. Minkin ◽  
O.V. Nikolaeva ◽  
A.A. Russkov
Keyword(s):  
Principal Component Analysis ◽  
Data Compression ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Gas Absorption ◽  
Absorption Bands ◽  
Spectral Bands ◽  
The Mean ◽  
Retrieval Errors

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

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