Jun‐Li Xu, C. Riccioli, A. Herrero-Langreo, A. Gowen
Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.
{"title":"Deep learning classifiers for near infrared spectral imaging: a tutorial","authors":"Jun‐Li Xu, C. Riccioli, A. Herrero-Langreo, A. Gowen","doi":"10.1255/jsi.2020.a19","DOIUrl":"https://doi.org/10.1255/jsi.2020.a19","url":null,"abstract":"Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43890045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present the progress made in developing multimodal and multispectral light microscopy for label-free malaria diagnosis. Our previously developed light emitting diode (LED) illumination system was replaced by laser diodes as light sources in order to narrow the spectral bands and improve the effectiveness of the contrast function for infected blood cell detection. The acquisition system is now equipped with an algorithm for automatic field scanning and best in-focus determination. We demonstrate the potential of this platform to provide multiple investigation modalities like transmission, reflection, scattering, fluorescence, excitation, emission and polarisation. The application of this platform on malaria-infected samples has shown the effectiveness of such a system in label-free and all-optical malaria detection by allowing the possibility of using a different type of imaging set-up for the samples analysed. Also, fewer illumination sources are used to characterise malaria-infected red blood cells compared to our previous works on malaria detection using LEDs illumination sources.
{"title":"Development of a multispectral microscopy platform using laser diode illumination for effective and automatic label-free Plasmodium falciparum detection","authors":"Yao Alvarez Kossonou, J. Zoueu","doi":"10.1255/jsi.2020.a17","DOIUrl":"https://doi.org/10.1255/jsi.2020.a17","url":null,"abstract":"In this paper, we present the progress made in developing multimodal and multispectral light microscopy for label-free malaria diagnosis. Our previously developed light emitting diode (LED) illumination system was replaced by laser diodes as light sources in order to narrow the spectral bands and improve the effectiveness of the contrast function for infected blood cell detection. The acquisition system is now equipped with an algorithm for automatic field scanning and best in-focus determination. We demonstrate the potential of this platform to provide multiple investigation modalities like transmission, reflection, scattering, fluorescence, excitation, emission and polarisation. The application of this platform on malaria-infected samples has shown the effectiveness of such a system in label-free and all-optical malaria detection by allowing the possibility of using a different type of imaging set-up for the samples analysed. Also, fewer illumination sources are used to characterise malaria-infected red blood cells compared to our previous works on malaria detection using LEDs illumination sources.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46802759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Al Ktash, O. Hauler, E. Ostertag, M. Brecht
Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.
{"title":"Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton","authors":"Mohammad Al Ktash, O. Hauler, E. Ostertag, M. Brecht","doi":"10.1255/jsi.2020.a18","DOIUrl":"https://doi.org/10.1255/jsi.2020.a18","url":null,"abstract":"Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42169554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carolina Blanch-Pérez del Notario, C. López-Molina, A. Lambrechts, W. Saeys
The discrimination power of a hyperspectral imaging system for image segmentation or object detection is determined by the illumination, the camera spatial–spectral resolution, and both the pre-processing and analysis methods used for image processing. In this study, we methodically reviewed the alternatives for each of those factors for a case study from the food industry to provide guidance in the construction and configuration of hyperspectral imaging systems in the visible near infrared range for food quality inspection. We investigated both halogen- and LED-based illuminations and considered cameras with different spatial–spectral resolution trade-offs. At the level of the data analysis, we evaluated the impact of binning, median filtering and bilateral filtering as pre- or post-processing and compared pixel-based classifiers with convolutional neural networks for a challenging application in the food industry, namely ingredient identification in a flour–seed mix. Starting from a basic configuration and by modifying the combination of system aspects we were able to increase the mean accuracy by at least 25 %. In addition, different trade-offs in performance-complexity were identified for different combinations of system parameters, allowing adaptation to diverse application requirements.
{"title":"Hyperspectral system trade-offs for illumination, hardware and analysis methods: a case study of seed mix ingredient discrimination","authors":"Carolina Blanch-Pérez del Notario, C. López-Molina, A. Lambrechts, W. Saeys","doi":"10.1255/jsi.2020.a16","DOIUrl":"https://doi.org/10.1255/jsi.2020.a16","url":null,"abstract":"The discrimination power of a hyperspectral imaging system for image segmentation or object detection is determined by the illumination, the camera spatial–spectral resolution, and both the pre-processing and analysis methods used for image processing. In this study, we methodically reviewed the alternatives for each of those factors for a case study from the food industry to provide guidance in the construction and configuration of hyperspectral imaging systems in the visible near infrared range for food quality inspection. We investigated both halogen- and LED-based illuminations and considered cameras with different spatial–spectral resolution trade-offs. At the level of the data analysis, we evaluated the impact of binning, median filtering and bilateral filtering as pre- or post-processing and compared pixel-based classifiers with convolutional neural networks for a challenging application in the food industry, namely ingredient identification in a flour–seed mix. Starting from a basic configuration and by modifying the combination of system aspects we were able to increase the mean accuracy by at least 25 %. In addition, different trade-offs in performance-complexity were identified for different combinations of system parameters, allowing adaptation to diverse application requirements.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43890105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Rangzan, S. Beyranvand, H. Pourkaseb, H. Ranjbar, A. Zarasvandi
An extensive series of volcanic rocks are exposed in the north of Saveh city, Iran, which consist of phyllic, argillic and propylitic hydrothermal alteration types. For the purpose of the investigation, a FieldSpec3® spectroradiometer was used to measure the spectral response of the mineral content of these rocks. The spectral analyses of reflectance curve by The Spectral Geologist (TSG) software could discriminate kaolinite and montmorillonite (argillic), illite, muscovite, phengite and paragonite (phyllic), hornblende and chlorite, siderite (propylitic), hematite and goethite from the gossans. It also detected gypsum of hydrothermal alteration zones. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image, which was used for mapping the hydrothermal alteration minerals, contains the Visible and Near Infrared (VNIR) wavelengths between 0.52 µm and 0.86 µm, Short Wave Infrared (SWIR) wavelengths between 1.6 µm and 2.43 µm and Thermal Infrared (TIR) wavelengths between 8.125 µm and 11.65 µm with 15, 30 and 90 m spatial resolutions, respectively. For calibration of the ASTER images, the extracted spectra of different rocks and minerals were used for atmospheric and radiometric corrections. Mixture tuned matched filtering (MTMF) and Spectral Angle Mapper (SAM) were applied on ASTER data to map the hydrothermal alteration of minerals. The use of the spectroradiometry techniques in conjunction with other data exhibits the ability of these new methods for non-destructive and rapid identification of mineral types for more detailed investigation. The results show that the area has undergone different levels of hydrothermal alteration, so much so that phyllic, argillic and propylitic types of hydrothermal alteration are present in the study area. This may point to high potential and promising zones for the exploration of porphyry mineralisation.
{"title":"Applying spectral analysis for identification of alteration zones in north Saveh area, Iran","authors":"K. Rangzan, S. Beyranvand, H. Pourkaseb, H. Ranjbar, A. Zarasvandi","doi":"10.1255/jsi.2020.a15","DOIUrl":"https://doi.org/10.1255/jsi.2020.a15","url":null,"abstract":"An extensive series of volcanic rocks are exposed in the north of Saveh city, Iran, which consist of phyllic, argillic and propylitic hydrothermal alteration types. For the purpose of the investigation, a FieldSpec3® spectroradiometer was used to measure the spectral response of the mineral content of these rocks. The spectral analyses of reflectance curve by The Spectral Geologist (TSG) software could discriminate kaolinite and montmorillonite (argillic), illite, muscovite, phengite and paragonite (phyllic), hornblende and chlorite, siderite (propylitic), hematite and goethite from the gossans. It also detected gypsum of hydrothermal alteration zones. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image, which was used for mapping the hydrothermal alteration minerals, contains the Visible and Near Infrared (VNIR) wavelengths between 0.52 µm and 0.86 µm, Short Wave Infrared (SWIR) wavelengths between 1.6 µm and 2.43 µm and Thermal Infrared (TIR) wavelengths between 8.125 µm and 11.65 µm with 15, 30 and 90 m spatial resolutions, respectively. For calibration of the ASTER images, the extracted spectra of different rocks and minerals were used for atmospheric and radiometric corrections. Mixture tuned matched filtering (MTMF) and Spectral Angle Mapper (SAM) were applied on ASTER data to map the hydrothermal alteration of minerals. The use of the spectroradiometry techniques in conjunction with other data exhibits the ability of these new methods for non-destructive and rapid identification of mineral types for more detailed investigation. The results show that the area has undergone different levels of hydrothermal alteration, so much so that phyllic, argillic and propylitic types of hydrothermal alteration are present in the study area. This may point to high potential and promising zones for the exploration of porphyry mineralisation.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42709003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In remote sensing, the compositional information of part of the earth’s surface is statistically evaluated by comparing known field or library spectra with the unknown image spectra, known as spectral matching or spectral similarity analysis. In this research, hybrid spectral similarity algorithms developed based on chi-square distance (CHI or χ2) are used to retrieve useful information from the Hyperion hyperspectral oil spill image covering the area near Liaodong Bay of the Bohai Sea, China. In order to evaluate the discriminability of spectral similarity algorithms, a pixel-level matching is carried out between the reference vectors, viz. Oil Slick (O), Sheen (H), Sea Water (S) and Ship Track (T), collected visually from known areas in the image. The hybrid spectral similarity algorithms are statistically assessed for their performance using the spectral discriminatory measures (i) relative spectral discriminatory power (RSDPW), (ii) relative spectral discriminatory probability (RSDPB) and (iii) relative spectral discriminatory entropy (RSDE). Additionally, the selected hybrid algorithms are used on the Hyperion image subset to perform a pixel-based classification. Classification results revealed that the CHI-based hybrid algorithms performed better than all other hybrid spectral similarity methods. Therefore, the CHI-based hybrid algorithms demonstrated their superior spectral discrimination capacity to classify marine spectral classes for oil spill mapping from the hyperspectral dataset.
在遥感中，通过将已知的场或库光谱与未知的图像光谱进行比较，对部分地球表面的成分信息进行统计评估，称为光谱匹配或光谱相似分析。本文采用基于χ2或χ2的混合光谱相似算法，对覆盖辽东湾附近海域的Hyperion高光谱溢油图像进行有用信息检索。为了评估光谱相似算法的可分辨性，对图像中已知区域视觉采集的参考向量Oil Slick (O)、Sheen (H)、Sea Water (S)和Ship Track (T)进行像素级匹配。利用光谱判别度量(i)相对光谱判别功率(RSDPW)、(ii)相对光谱判别概率(RSDPB)和(iii)相对光谱判别熵(RSDE)对混合光谱相似算法的性能进行统计评估。此外，在Hyperion图像子集上使用所选择的混合算法来执行基于像素的分类。分类结果表明，基于chi的混合光谱相似度算法优于其他混合光谱相似度方法。因此，基于chi的混合算法显示出其优越的光谱识别能力，可以从高光谱数据集中对海洋光谱类别进行分类，用于溢油映射。
{"title":"Spectral similarity algorithm-based image classification for oil spill mapping of hyperspectral datasets","authors":"Deepthi, T. Thomas","doi":"10.1255/jsi.2020.a14","DOIUrl":"https://doi.org/10.1255/jsi.2020.a14","url":null,"abstract":"In remote sensing, the compositional information of part of the earth’s surface is statistically evaluated by comparing known field or library spectra with the unknown image spectra, known as spectral matching or spectral similarity analysis. In this research, hybrid spectral similarity algorithms developed based on chi-square distance (CHI or χ2) are used to retrieve useful information from the Hyperion hyperspectral oil spill image covering the area near Liaodong Bay of the Bohai Sea, China. In order to evaluate the discriminability of spectral similarity algorithms, a pixel-level matching is carried out between the reference vectors, viz. Oil Slick (O), Sheen (H), Sea Water (S) and Ship Track (T), collected visually from known areas in the image. The hybrid spectral similarity algorithms are statistically assessed for their performance using the spectral discriminatory measures (i) relative spectral discriminatory power (RSDPW), (ii) relative spectral discriminatory probability (RSDPB) and (iii) relative spectral discriminatory entropy (RSDE). Additionally, the selected hybrid algorithms are used on the Hyperion image subset to perform a pixel-based classification. Classification results revealed that the CHI-based hybrid algorithms performed better than all other hybrid spectral similarity methods. Therefore, the CHI-based hybrid algorithms demonstrated their superior spectral discrimination capacity to classify marine spectral classes for oil spill mapping from the hyperspectral dataset.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45660644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Russo, C. Heaton, Lucy Flint, O. Voloaca, S. Haywood-Small, M. Clench, S. Francese, L. Cole
Mass spectrometry imaging (MSI) is a powerful and versatile technique able to investigate the spatial distribution of multiple non-labelled endogenous and exogenous analytes simultaneously, within a wide range of samples. Over the last two decades, MSI has found widespread application for an extensive range of disciplines including pre-clinical drug discovery, clinical applications and human identification for forensic purposes. Technical advances in both instrumentation and software capabilities have led to a continual increase in the interest in MSI; however, there are still some limitations. In this review, we discuss the emerging applications in MSI that significantly impact three key areas of mass spectrometry (MS) research—clinical, pre-clinical and forensics—and roadblocks to the expansion of use of MSI in these areas.
{"title":"Emerging applications in mass spectrometry imaging; enablers and roadblocks","authors":"C. Russo, C. Heaton, Lucy Flint, O. Voloaca, S. Haywood-Small, M. Clench, S. Francese, L. Cole","doi":"10.1255/jsi.2020.a13","DOIUrl":"https://doi.org/10.1255/jsi.2020.a13","url":null,"abstract":"Mass spectrometry imaging (MSI) is a powerful and versatile technique able to investigate the spatial distribution of multiple non-labelled endogenous and exogenous analytes simultaneously, within a wide range of samples. Over the last two decades, MSI has found widespread application for an extensive range of disciplines including pre-clinical drug discovery, clinical applications and human identification for forensic purposes. Technical advances in both instrumentation and software capabilities have led to a continual increase in the interest in MSI; however, there are still some limitations. In this review, we discuss the emerging applications in MSI that significantly impact three key areas of mass spectrometry (MS) research—clinical, pre-clinical and forensics—and roadblocks to the expansion of use of MSI in these areas.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47627625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An elevated human presence due to the involvement of the coastal oases of Tunisia in the global petrochemical industry and population pressure in the 1970s has resulted in major changes in the oases’ agro–ecosystem environment. The consequences of this have been urbanisation and rural exodus, priority to the industrial sectors and services at the expense of agriculture, high mobility and rise of trade. The coastal oases of Gabes located in the South-East of Tunisia are considered in this study. This has been affected by sharp degradation, mainly of anthropogenic origins such as demographic growth, extension of the urban areas and creation of a highly contaminating chemical zone amplifying their environmental vulnerability. Satellite data is an essential tool in the study and mapping of these types of environment and for that, we started with the mapping of the vegetative land use using the vegetation indices derived from the hyperspectral scene of the Hyperion sensor (25 April 2010) and field data. This has allowed us to better characterise the most vulnerable areas and to identify the socio–environmental risks. The analysis of the radiometric indices leads to the definition of the spatial extension of vegetation cover in the oases. This study has permitted us to outline the oases’ typologies in Gabes and to discuss their dynamics in the short term.
{"title":"Spatial analysis of Hyperion hyperspectral indices to map the vegetation state in the coastal oases of Tunisia","authors":"Rim Katlane, J. Bergès, G. Beltrando","doi":"10.1255/jsi.2020.a11","DOIUrl":"https://doi.org/10.1255/jsi.2020.a11","url":null,"abstract":"An elevated human presence due to the involvement of the coastal oases of Tunisia in the global petrochemical industry and population pressure in the 1970s has resulted in major changes in the oases’ agro–ecosystem environment. The consequences of this have been urbanisation and rural exodus, priority to the industrial sectors and services at the expense of agriculture, high mobility and rise of trade. The coastal oases of Gabes located in the South-East of Tunisia are considered in this study. This has been affected by sharp degradation, mainly of anthropogenic origins such as demographic growth, extension of the urban areas and creation of a highly contaminating chemical zone amplifying their environmental vulnerability. Satellite data is an essential tool in the study and mapping of these types of environment and for that, we started with the mapping of the vegetative land use using the vegetation indices derived from the hyperspectral scene of the Hyperion sensor (25 April 2010) and field data. This has allowed us to better characterise the most vulnerable areas and to identify the socio–environmental risks. The analysis of the radiometric indices leads to the definition of the spatial extension of vegetation cover in the oases. This study has permitted us to outline the oases’ typologies in Gabes and to discuss their dynamics in the short term.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42865438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work we propose an efficient approach to image segmentation for multispectral images of unstained blood films and automatic counting of erythrocytes. Our method takes advantage of Beer–Lambert’s law by using, first, a statistical standardisation equation applied to transmittance images, followed by the local adaptive threshold to detect the blood cells and hysteresis contour closing to obtain the complete blood cell boundaries, and finally the watershed algorithm is used. With this method, image pre-processing is not required, which leads to time savings. We obtained the following results that show that our technique is effective, efficient and fast: Precision of 98.47 % and Recall of 98.23 %, a degree of precision (F-Measurement) of 98.34 % and an Accuracy of 96.75 %.
{"title":"Detection and segmentation of erythrocytes in multispectral label-free blood smear images for automatic cell counting","authors":"Solange Doumun, Sophie Dabo, J. Zoueu","doi":"10.1255/jsi.2020.a10","DOIUrl":"https://doi.org/10.1255/jsi.2020.a10","url":null,"abstract":"In this work we propose an efficient approach to image segmentation for multispectral images of unstained blood films and automatic counting of erythrocytes. Our method takes advantage of Beer–Lambert’s law by using, first, a statistical standardisation equation applied to transmittance images, followed by the local adaptive threshold to detect the blood cells and hysteresis contour closing to obtain the complete blood cell boundaries, and finally the watershed algorithm is used. With this method, image pre-processing is not required, which leads to time savings. We obtained the following results that show that our technique is effective, efficient and fast: Precision of 98.47 % and Recall of 98.23 %, a degree of precision (F-Measurement) of 98.34 % and an Accuracy of 96.75 %.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45760444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Raman confocal microscopy is an increasingly useful technique when applied to food samples; it has the unique ability to interrogate the chemical structure, aligned with the same confocality capability that is available when using standard confocal microscopy. In this research, we investigated the potential of Raman confocal microscopy to investigate the components in a model cheese system. We showed an ability to distinguish whey protein particles within the casein protein matrix using several different image analysis approaches. The results illustrate the potential of Raman confocal microscopy and imaging to understand the chemical and microstructural features of cheese systems via the analysis of the distribution of the protein types in complex dairy matrices.
{"title":"Raman imaging of protein in a model cheese system","authors":"E. Nickless, S. Holroyd","doi":"10.1255/jsi.2020.a9","DOIUrl":"https://doi.org/10.1255/jsi.2020.a9","url":null,"abstract":"Raman confocal microscopy is an increasingly useful technique when applied to food samples; it has the unique ability to interrogate the chemical structure, aligned with the same confocality capability that is available when using standard confocal microscopy. In this research, we investigated the potential of Raman confocal microscopy to investigate the components in a model cheese system. We showed an ability to distinguish whey protein particles within the casein protein matrix using several different image analysis approaches. The results illustrate the potential of Raman confocal microscopy and imaging to understand the chemical and microstructural features of cheese systems via the analysis of the distribution of the protein types in complex dairy matrices.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41877138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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