scholarly journals HYPERSPECTRAL CLASSIFICATION FOR IDENTIFYING DECAYED ORANGES INFECTED BY FUNGI

2017 ◽  
pp. 601 ◽  
Author(s):  
Shiyang Yin ◽  
Xiaoqing Bi ◽  
Yong Niu ◽  
Xiaomin Gu ◽  
Yong Xiao
Keyword(s):  
Near Infrared ◽  
Color Image ◽  
Wavelength Region ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Multispectral Image ◽  
Identification Accuracy ◽  
Threshold Method ◽  
Characteristic Wavelength ◽  
Pseudo Color

Fast and nondestructive detection of early decay caused by fungal infection in citrus fruit was a challenging task for the citrus industry during the postharvest fruit processing. In general, workers relied on the ultraviolet induction fluorescence technique to detect and remove the decayed citrus fruits in fruit packing houses. However, this operation was harmful for human health, and was also very inefficient. In this study, navel oranges were used as research object. A novel method combining with hyperspectral imaging technology in the wavelength region between 400 and 1100 nm wavelength was proposed to solve this problem. First, normalization approaches were applied to decrease the variation of spectral reflectance intensity due to natural curvature of navel orange surface. Then, the spectral data of regions of interest (ROIs) from normal and decayed tissues was analyzed by principal component analysis (PCA) for investigating the performance of visible and near infrared (Vis-NIR) hyperspectral data to discriminate these two kinds of tissues. Next, six characteristic wavelength images were obtained by analyzing the loadings of the first principal component (PC1). And, a multispectral image was established by using the corrected six characteristic wavelength images. On basis of the multispectral image, pseudo-color image processing with intensity slicing was utilized to produce a two-dimensional color image with clear contrast between decayed and normal tissues. Finally, an image segmentation algorithm by combining the pseudo-color processing method and a global threshold method was proposed for fast identification of decayed navel oranges. For 240 independent samples, the success rates were 100 and 97.5% for decayed navel oranges infected by Penicillium digitatum and normal navel oranges, respectively. In particular, the proposed algorithm was also applied to detect the decayed navel oranges infected by Penicillium italicum (samples not used for the development of algorithm) and obtained a 91.7% identification accuracy, indicating a well generalization ability and actual application value of the proposed algorithm.

scholarly journals Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Zhang ◽  
Biyao Wang ◽  
Pengtao Yan ◽  
Kunlun Wang ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Injection ◽  
Principal Component ◽  
Local Extremum ◽  
Hyperspectral Data ◽  
Identification Accuracy ◽  
Hyperspectral Images ◽  
Nondestructive Identification ◽  
Som Network ◽  
Weighting Coefficients

For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%.

scholarly journals Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing

Minerals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 809 ◽  
Author(s):  
Natsuo Okada ◽  
Yohei Maekawa ◽  
Narihiro Owada ◽  
Kazutoshi Haga ◽  
Atsushi Shibayama ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Visual Information ◽  
Near Infrared ◽  
Wavelength Region ◽  
Mineral Processing ◽  
Hyperspectral Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Mining Operations

In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%.

scholarly journals Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Ning Cao ◽  
Shuqiang Lyu ◽  
Miaole Hou ◽  
Wanfu Wang ◽  
Zhenhua Gao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Environmental Changes ◽  
Color Image ◽  
Visual Quality ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Infrared Band ◽  
Dark Channel ◽  
Restoration Method ◽  
Pseudo Color

AbstractEnvironmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to produce a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the images restored by the proposed method are superior in variance, average gradient, information entropy and gray scale contrast comparing to the results from the traditional methods of homomorphic filtering and Gaussian stretching. The results also show the highest score in comprehensive evaluation of edges, hue and structure; thus, the method proposed can support more potential studies or sootiness removal in real mural paintings with more detailed information. The method proposed shows strong evidence that it can effectively reduce the influence of sootiness on the moral images with more details that can reveal the original appearance of the mural and improve its visual quality.

scholarly journals Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haifeng Sima ◽  
Pei Liu ◽  
Lanlan Liu ◽  
Aizhong Mi ◽  
Jianfang Wang
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Color Image ◽  
Matching Pursuit ◽  
Spatial Coherence ◽  
Principal Component ◽  
Distance Constraint ◽  
Sparse Representation Classification ◽  
Simple Linear Iterative Clustering ◽  
Pseudo Color

Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification method is investigated by flexible patches sampling of superpixels. First, the principal component analysis and total variation diffusion are employed to form the pseudo color image for simplifying superpixels computing with (simple linear iterative clustering) SLIC model. Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse characteristics of test pixel, which are carried out on the orthogonal matching pursuit (OMP) algorithm. At last, the pixel is labeled according to the minimum distance constraint for final classification based on the joint sparse coefficients and structured dictionary. Experiments conducted on two real hyperspectral datasets show the superiority and effectiveness of the proposed method.

scholarly journals Restoration Method of Sootiness Mural Images Based on Dark Channel Prior and Retinex by Bilateral Filter

2020 ◽  
Author(s):  
Ning Cao ◽  
Shuqiang Lyu ◽  
Miaole Hou ◽  
Wanfu Wang ◽  
Zhenhua Gao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Environmental Changes ◽  
Color Image ◽  
Visual Quality ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Infrared Band ◽  
Dark Channel ◽  
Restoration Method ◽  
Pseudo Color

Abstract Environmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to synthesize a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the proposed method can effectively reduce the influence of sootiness on the mural image and improve its visual quality. It can also be used to reveal the original appearance of the mural to reasonable extent.

scholarly journals The Sloan Digital Sky Survey Quasar Catalog: Fourteenth data release

2018 ◽  
Vol 613 ◽  
pp. A51 ◽  
Author(s):  
Isabelle Pâris ◽  
Patrick Petitjean ◽  
Éric Aubourg ◽  
Adam D. Myers ◽  
Alina Streblyanska ◽  
...  
Keyword(s):  
Emission Line ◽  
Near Infrared ◽  
Wavelength Region ◽  
Principal Component ◽  
Sloan Digital Sky Survey ◽  
Large Area ◽  
Robust Identification ◽  
Broad Absorption Line ◽  
Sky Survey ◽  
Data Release

We present the data release 14 Quasar catalog (DR14Q) from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey IV (SDSS-IV). This catalog includes all SDSS-IV/eBOSS objects that were spectroscopically targeted as quasar candidates and that are confirmed as quasars via a new automated procedure combined with a partial visual inspection of spectra, have luminosities Mi [z = 2] < −20.5 (in a Λ CDM cosmology with H0 = 70 km s−1 Mpc−1, Ω M =0.3, and Ω Λ = 0.7), and either display at least one emission line with a full width at half maximum larger than 500 km s−1 or, if not, have interesting/complex absorption features. The catalog also includes previously spectroscopically-confirmed quasars from SDSS-I, II, and III. The catalog contains 526 356 quasars (144 046 are new discoveries since the beginning of SDSS-IV) detected over 9376 deg2 (2044 deg2 having new spectroscopic data available) with robust identification and redshift measured by a combination of principal component eigenspectra. The catalog is estimated to have about 0.5% contamination. Redshifts are provided for the Mg II emission line. The catalog identifies 21 877 broad absorption line quasars and lists their characteristics. For each object, the catalog presents five-band (u, g, r, i, z) CCD-based photometry with typical accuracy of 0.03 mag. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra, covering the wavelength region 3610–10 140 Å at a spectral resolution in the range 1300 < R < 2500, can be retrieved from the SDSS Science Archiver Server.

scholarly journals Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1527
Author(s):  
Xi Pan ◽  
Kang Li ◽  
Zhangjing Chen ◽  
Zhong Yang
Keyword(s):  
Near Infrared ◽  
Spatial Clustering ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Image Features ◽  
Identification Accuracy ◽  
Support Vector ◽  
Gray Level ◽  
Wood Identification

Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780–2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780–1100 nm and 1100–2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features.

scholarly journals Rapid identification of wood species by near-infrared spatially resolved spectroscopy (NIR-SRS) based on hyperspectral imaging (HSI)

Holzforschung ◽  
2019 ◽  
Vol 73 (4) ◽  
pp. 323-330 ◽  
Author(s):  
Te Ma ◽  
Tetsuya Inagaki ◽  
Mayuka Ban ◽  
Satoru Tsuchikawa
Keyword(s):  
Light Scattering ◽  
Hyperspectral Imaging ◽  
Wood Species ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Rapid Identification ◽  
Identification Accuracy ◽  
Image Histogram ◽  
Halogen Light

AbstractConventional near-infrared (NIR) spectroscopy has shown its potential to separate wood species nondestructively based on the aggregate effect of light absorption and scattering values. However, wood has an aligned microstructure, and there is a large refractive index (RI) mismatch between the wood cell wall substance (n≈1.55) and the cell lumen (air≈1.0, water≈1.33). Light scattering is dominant over absorption$({\mu '_s} \gg {\mu _a})$in wood, and this fact can be utilized for complex classification purposes. In this study, an NIR hyperspectral imaging (HSI) camera combined with one focused halogen light source (Ø 1 mm) was designed to evaluate the light scattering patterns of five softwood (SW) and 10 hardwood (HW) species in the wavelength range from 1002 to 2130 nm. Several parameters were combined to improve the data quality, such as image histogram plots of defined spaced bins (associated with diffuse reflectance values of light), variance calculation on the frequency (the number of pixels in each bin) of each histogram and the principal component analysis (PCA) of all the variance values at each wavelength. The identification accuracy of the quadratic discriminant analysis (QDA) under the five-fold cross-validation method was 94.1%, based on the first three principal component (PC) scores.

scholarly journals Algorithms for detecting cherry pits on the basis of transmittance mode hyperspectral data

2017 ◽  
Vol 31 (4) ◽  
pp. 539-549 ◽  
Author(s):  
Anna Siedliska ◽  
Monika Zubik ◽  
Piotr Baranowski ◽  
Wojciech Mazurek
Keyword(s):  
Near Infrared ◽  
Imaging Technique ◽  
Principal Component ◽  
Solid Content ◽  
Hyperspectral Data ◽  
Infrared Range ◽  
Soluble Solid Content ◽  
Soluble Solid ◽  
Pre Treatment ◽  
Validation Set

Abstract The suitability of the hyperspectral transmittance imaging technique was assessed in terms of detecting the internal intrusions (pits and their fragments) in cherries. Herein, hyperspectral transmission images were acquired in the visible and near-infrared range (450-1000 nm) from pitted and intact cherries of three popular cultivars: ‘Łutówka’, ‘Pandy 103’, and ‘Groniasta’, differing by soluble solid content. The hyperspectral transmittance data of fresh cherries were used to determine the influence of differing soluble solid content in fruit tissues on pit detection effectiveness. Models for predicting the soluble solid content of cherries were also developed. The principal component analysis and the second derivative pre-treatment of the hyperspectral data were used to construct the supervised classification models. In this study, five classifiers were tested for pit detection. From all the classifiers studied, the best prediction accuracies for the whole pit or pit fragment detection were obtained via the backpropagation neural networks model (87.6% of correctly classified instances for the training/test set and 81.4% for the validation set). The accuracy of distinguishing between drilled and intact cherries was close to 96%. These results showed that the hyperspectral transmittance imaging technique is feasible and useful for the non-destructive detection of pits in cherries.

scholarly journals Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques

2019 ◽  
Vol 12 (1) ◽  
pp. 63
Author(s):  
Mozhgan Abbasi ◽  
Jochem Verrelst ◽  
Mohsen Mirzaei ◽  
Safar Marofi ◽  
Hamid Reza Riyahi Bakhtiari
Keyword(s):  
Precision Agriculture ◽  
Tree Species ◽  
Near Infrared ◽  
Principal Component ◽  
Field Measurements ◽  
Hyperspectral Data ◽  
Visible Range ◽  
Species Discrimination ◽  
Multivariate Statistical ◽  
Significant Difference

Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm.

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