Hyperspectral imaging (HSI) is a popular mode of remote sensing imaging that collects data beyond the visible spectrum. Many classification techniques have been developed in recent years, since classification is the most crucial task in hyperspectral image processing. Furthermore, extracting features from hyperspectral images is challenging in many scenarios. The semi-supervised classification of HSI is motivated by the Cycle-GAN method that has been proposed in this research paper. Since the proposed HSI classification method is semi-supervised, it makes extensive use of the labelled samples, which are short and have numerous unlabelled images. The research is carried out in two phases. First, to extract the spectral–spatial features, the minimum noise fraction is adopted. And, second, the classification of the semi-supervised method is done by the cycle-GANs. Subsequently, the proposed architecture is implemented on three standard hyperspectral dataset methods. As a result, the performance comparison is carried out in the same field as state-of-the-art approaches. The obtained results successfully demonstrate the supremacy of the proposed technique in the classification of HSI.
{"title":"A semi-supervised cycle-GAN neural network for hyperspectral image classification with minimum noise fraction","authors":"T. Reddy, J. Harikiran","doi":"10.1255/jsi.2022.a2","DOIUrl":"https://doi.org/10.1255/jsi.2022.a2","url":null,"abstract":"Hyperspectral imaging (HSI) is a popular mode of remote sensing imaging that collects data beyond the visible spectrum. Many classification techniques have been developed in recent years, since classification is the most crucial task in hyperspectral image processing. Furthermore, extracting features from hyperspectral images is challenging in many scenarios. The semi-supervised classification of HSI is motivated by the Cycle-GAN method that has been proposed in this research paper. Since the proposed HSI classification method is semi-supervised, it makes extensive use of the labelled samples, which are short and have numerous unlabelled images. The research is carried out in two phases. First, to extract the spectral–spatial features, the minimum noise fraction is adopted. And, second, the classification of the semi-supervised method is done by the cycle-GANs. Subsequently, the proposed architecture is implemented on three standard hyperspectral dataset methods. As a result, the performance comparison is carried out in the same field as state-of-the-art approaches. The obtained results successfully demonstrate the supremacy of the proposed technique in the classification of HSI.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43314518","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}
The objective of this study is to compare portable visible spectral imaging (443–726 nm) and conventional RGB imaging for detecting products stored beyond the recommended “use-by” date and predicting the number of days poultry products have been stored. Packages of chicken thighs with skin on were stored at 4 °C and imaged daily in pack through plastic lidding film using spectral and RGB imaging over 10 days. K-nearest neighbour (KNN) models were built to detect poultry stored beyond its recommended “use-by” date and partial least squares regression (PLSR) models were built to predict the storage day of samples. Model overfitting in the spectral PLSR model was prevented using a geostatistical approach to estimate the number of latent variables (LV). All models were built at the object level by using mean spectra and colour values per image. The KNN model built using spectral images (acc. = 93 %, sen. = 75 %, spec. = 100 %) was more suitable than the model built using RGB images (acc. = 80 %, sen. = 42 %, spec. = 96 %) for detecting poultry stored beyond its “use-by” date. The PLSR model built using spectral images (R2 = 0.78 RMSEC = 0.92, RMSEV = 1.11, RMSEP = 1.34 day) was more suitable than the model built using RGB images (R2 = 0.60, RMSEC = 1.66, RMSEV = 1.67, RMSEP = 1.92 day) for predicting storage day of poultry products.
{"title":"Comparison of portable spectral imaging (443–726 nm) and RGB imaging for predicting poultry product “use-by” status through packaging film","authors":"Anastasia Swanson, A. Herrero-Langreo, A. Gowen","doi":"10.1255/jsi.2021.a6","DOIUrl":"https://doi.org/10.1255/jsi.2021.a6","url":null,"abstract":"The objective of this study is to compare portable visible spectral imaging (443–726 nm) and conventional RGB imaging for detecting products stored beyond the recommended “use-by” date and predicting the number of days poultry products have been stored. Packages of chicken thighs with skin on were stored at 4 °C and imaged daily in pack through plastic lidding film using spectral and RGB imaging over 10 days. K-nearest neighbour (KNN) models were built to detect poultry stored beyond its recommended “use-by” date and partial least squares regression (PLSR) models were built to predict the storage day of samples. Model overfitting in the spectral PLSR model was prevented using a geostatistical approach to estimate the number of latent variables (LV). All models were built at the object level by using mean spectra and colour values per image. The KNN model built using spectral images (acc. = 93 %, sen. = 75 %, spec. = 100 %) was more suitable than the model built using RGB images (acc. = 80 %, sen. = 42 %, spec. = 96 %) for detecting poultry stored beyond its “use-by” date. The PLSR model built using spectral images (R2 = 0.78 RMSEC = 0.92, RMSEV = 1.11, RMSEP = 1.34 day) was more suitable than the model built using RGB images (R2 = 0.60, RMSEC = 1.66, RMSEV = 1.67, RMSEP = 1.92 day) for predicting storage day of poultry products.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46638348","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}
M. Aref, A. Hussein, A. Youssef, Ibrahim H. Aboughaleb, Amr A. Sharawi, P. Saccomandi, Y. El-Sharkawy
Thermal ablation modalities, for example radiofrequency ablation (RFA) and microwave ablation, are intended to prompt controlled tumour removal by raising tissue temperature. However, monitoring the size of the resulting tissue damage during the thermal removal procedures is a challenging task. The objective of this study was to evaluate the observation of RFA on an ex vivo liver sample with both a commercial and a low-cost system to distinguish between the normal and the ablated regions as well as the thermally affected regions. RFA trials were conducted on five different ex vivo normal bovine samples and monitored initially by a custom hyperspectral (HS) camera to measure the diffuse reflectance (Rd) utilising a polychromatic light source (tungsten halogen lamp) within the spectral range 348–950 nm. Next, the light source was replaced with monochromatic LEDs (415, 565 and 660 nm) and a commercial charge-coupled device (CCD) camera was used instead of the HS camera. The system algorithm comprises image enhancement (normalisation and moving average filter) and image segmentation with K-means clustering, combining spectral and spatial information to assess the variable responses to polychromatic light and monochromatic LEDs to highlight the differences in the Rd properties of thermally affected/normal tissue regions. The measured spectral signatures of the various regions, besides the calculation of the standard deviations (δ) between the generated six groups, guided us to select three optimal wavelengths (420, 540 and 660 nm) to discriminate between these various regions. Next, we selected six spectral images to apply the image processing to (at 450, 500, 550, 600, 650 and 700 nm). We noticed that the optimum image is the superimposed spectral images at 550, 600, 650 and 700 nm, which are capable of discriminating between the various regions. Later, we measured Rd with the CCD camera and commercially available monochromatic LED light sources at 415, 565 and 660 nm. Compared to the HS camera results, this system was more capable of identifying the ablated and the thermally affected regions of surface RFA than the side-penetration RFA of the investigated ex vivo liver samples. However, we succeeded in developing a low-cost system that provides satisfactory information to highlight the ablated and thermally affected region to improve the outcome of surgical tumour ablation with much shorter time for image capture and processing compared to the HS system.
{"title":"Prospective study for commercial and low-cost hyperspectral imaging systems to evaluate thermal tissue effect on bovine liver samples","authors":"M. Aref, A. Hussein, A. Youssef, Ibrahim H. Aboughaleb, Amr A. Sharawi, P. Saccomandi, Y. El-Sharkawy","doi":"10.1255/jsi.2021.a5","DOIUrl":"https://doi.org/10.1255/jsi.2021.a5","url":null,"abstract":"Thermal ablation modalities, for example radiofrequency ablation (RFA) and microwave ablation, are intended to prompt controlled tumour removal by raising tissue temperature. However, monitoring the size of the resulting tissue damage during the thermal removal procedures is a challenging task. The objective of this study was to evaluate the observation of RFA on an ex vivo liver sample with both a commercial and a low-cost system to distinguish between the normal and the ablated regions as well as the thermally affected regions. RFA trials were conducted on five different ex vivo normal bovine samples and monitored initially by a custom hyperspectral (HS) camera to measure the diffuse reflectance (Rd) utilising a polychromatic light source (tungsten halogen lamp) within the spectral range 348–950 nm. Next, the light source was replaced with monochromatic LEDs (415, 565 and 660 nm) and a commercial charge-coupled device (CCD) camera was used instead of the HS camera. The system algorithm comprises image enhancement (normalisation and moving average filter) and image segmentation with K-means clustering, combining spectral and spatial information to assess the variable responses to polychromatic light and monochromatic LEDs to highlight the differences in the Rd properties of thermally affected/normal tissue regions. The measured spectral signatures of the various regions, besides the calculation of the standard deviations (δ) between the generated six groups, guided us to select three optimal wavelengths (420, 540 and 660 nm) to discriminate between these various regions. Next, we selected six spectral images to apply the image processing to (at 450, 500, 550, 600, 650 and 700 nm). We noticed that the optimum image is the superimposed spectral images at 550, 600, 650 and 700 nm, which are capable of discriminating between the various regions. Later, we measured Rd with the CCD camera and commercially available monochromatic LED light sources at 415, 565 and 660 nm. Compared to the HS camera results, this system was more capable of identifying the ablated and the thermally affected regions of surface RFA than the side-penetration RFA of the investigated ex vivo liver samples. However, we succeeded in developing a low-cost system that provides satisfactory information to highlight the ablated and thermally affected region to improve the outcome of surgical tumour ablation with much shorter time for image capture and processing compared to the HS system.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49158559","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}
Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.
烤烟(Nicotiana tabacum L.)是一种每英亩高价值的作物，通过集约化管理来优化高质量烤烟叶的产量。一项为期15天的研究评估了利用高光谱反射数据检测烟疫霉(黑胫)在烤烟中的发病率的潜力。高光谱反射数据取自一个黑胫病不断蔓延的商业烤烟田。这项努力包括两个关键目标。首先，开发高光谱指数和/或机器学习分类模型，能够检测烟草疫霉(黑胫)在烤烟中的发病率。其次，评估该模型区分症状前植物和健康植物的能力。基于无症状烤烟与有黑胫病症状烤烟光谱分布的差异，开发了两种高光谱指数来检测黑胫病的发生。其中一个指标为宽波段指标，另一个指标为窄波段指标，但两种指标之间的统计差异不显著，均能准确分类对症植物。进一步分析表明，健康植株与对症植株的指数差异有统计学意义(α = 0.05)。此外，这些指标能够在症状前检测到黑胫病(α = 0.09)。子空间线性判别分析(Subspace linear discriminant analysis，一种机器学习分类)也被用于预测黑胫病的发病率，分类准确率高达85.7%。讨论了使用光谱指数或机器学习进行分类对未来黑胫研究的意义。
{"title":"Hyperspectral reflectance for non-invasive early detection of black shank disease in flue-cured tobacco","authors":"A. Hayes, T. D. Reed","doi":"10.1255/jsi.2021.a4","DOIUrl":"https://doi.org/10.1255/jsi.2021.a4","url":null,"abstract":"Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48005789","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}
Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.
{"title":"Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models","authors":"B. Devassy, S. George","doi":"10.1255/jsi.2021.a3","DOIUrl":"https://doi.org/10.1255/jsi.2021.a3","url":null,"abstract":"Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47484198","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}
The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.
{"title":"1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures","authors":"C. Murphy, J. Kerekes","doi":"10.1255/JSI.2021.A2","DOIUrl":"https://doi.org/10.1255/JSI.2021.A2","url":null,"abstract":"The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66247337","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}
Pub Date : 2021-03-24DOI: 10.1109/WHISPERS52202.2021.9484038
Deependra Mishra, Helena Hurbon, John Wang, Steven T. Wang, Tommy Du, Qian Wu, David Kim, Shiva Basir, Qian Cao, Hairong Zhang, Kathleen Xu, Andy Yu, Yifan Zhang, Yunshen Huang, Roman Garrett, Maria Gerasimchuk-Djordjevic, Mikhail Y. Berezin
Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical and machine vision fields. The rapidly increasing number of applications requires a convenient easy-to-navigate software that can be used by new and experienced users to analyze data, develop, apply, and deploy novel algorithms. Herein, we present our platform, IDCube that performs essential operations in hyperspectral data analysis to realize the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimize parameters and obtain visual input for the user. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new hidden features.
{"title":"Idcube Lite – A Free Interactive Discovery Cube Software for Multi And Hyperspectral Applications","authors":"Deependra Mishra, Helena Hurbon, John Wang, Steven T. Wang, Tommy Du, Qian Wu, David Kim, Shiva Basir, Qian Cao, Hairong Zhang, Kathleen Xu, Andy Yu, Yifan Zhang, Yunshen Huang, Roman Garrett, Maria Gerasimchuk-Djordjevic, Mikhail Y. Berezin","doi":"10.1109/WHISPERS52202.2021.9484038","DOIUrl":"https://doi.org/10.1109/WHISPERS52202.2021.9484038","url":null,"abstract":"Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical and machine vision fields. The rapidly increasing number of applications requires a convenient easy-to-navigate software that can be used by new and experienced users to analyze data, develop, apply, and deploy novel algorithms. Herein, we present our platform, IDCube that performs essential operations in hyperspectral data analysis to realize the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimize parameters and obtain visual input for the user. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new hidden features.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62938175","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}
Pub Date : 2021-01-01Epub Date: 2021-05-05DOI: 10.1255/jsi.2021.a1
Deependra Mishra, Helena Hurbon, John Wang, Steven T Wang, Tommy Du, Qian Wu, David Kim, Shiva Basir, Qian Cao, Hairong Zhang, Kathleen Xu, Andy Yu, Yifan Zhang, Yunshen Huang, Roman Garnett, Maria Gerasimchuk-Djordjevic, Mikhail Y Berezin
Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new, hidden features that allow users to integrate novel computational methods.
{"title":"IDCube Lite: Free Interactive Discovery Cube software for multi- and hyperspectral applications.","authors":"Deependra Mishra, Helena Hurbon, John Wang, Steven T Wang, Tommy Du, Qian Wu, David Kim, Shiva Basir, Qian Cao, Hairong Zhang, Kathleen Xu, Andy Yu, Yifan Zhang, Yunshen Huang, Roman Garnett, Maria Gerasimchuk-Djordjevic, Mikhail Y Berezin","doi":"10.1255/jsi.2021.a1","DOIUrl":"https://doi.org/10.1255/jsi.2021.a1","url":null,"abstract":"<p><p>Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new, hidden features that allow users to integrate novel computational methods.</p>","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":"10 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39386465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is highly demanding to identify healthy and non-healthy species in a heterogeneous environment such as human tissues. In such a case, one identifier, such as a spectral fingerprint, might be inadequate. Therefore, additional identification is required, for instance, a polarisation measurement. In view of that, the development of a spectropolarimeter that captures two cross-polarised arrays of spectral images is a key requirement. To meet this requirement, an imager optical setup has been designed to provide spatial, spectral and polarisation preference information for species that exist in a heterogeneous environment, such as in medical tissue samples. The spectral and polarisation information is obtained employing an acousto-optic tunable filter and a polarising beam splitter, respectively. The optical imager is designed to operate in the visible-near infrared range (450–850 nm) with a spectral resolution of 3 nm. The spectropolarimeter design along with optical characterisation results are reported.
{"title":"A novel dual-path high-throughput acousto-optic tunable filter imaging spectropolarimeter","authors":"R. Abdlaty, Q. Fang","doi":"10.1255/jsi.2020.a20","DOIUrl":"https://doi.org/10.1255/jsi.2020.a20","url":null,"abstract":"It is highly demanding to identify healthy and non-healthy species in a heterogeneous environment such as human tissues. In such a case, one identifier, such as a spectral fingerprint, might be inadequate. Therefore, additional identification is required, for instance, a polarisation measurement. In view of that, the development of a spectropolarimeter that captures two cross-polarised arrays of spectral images is a key requirement. To meet this requirement, an imager optical setup has been designed to provide spatial, spectral and polarisation preference information for species that exist in a heterogeneous environment, such as in medical tissue samples. The spectral and polarisation information is obtained employing an acousto-optic tunable filter and a polarising beam splitter, respectively. The optical imager is designed to operate in the visible-near infrared range (450–850 nm) with a spectral resolution of 3 nm. The spectropolarimeter design along with optical characterisation results are reported.","PeriodicalId":37385,"journal":{"name":"Journal of Spectral Imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43392207","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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