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Journal articles on the topic 'Hyperspectral imaging'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

V, Prathama, and Dr Thippeswamy G. "Food Safety Control Using Hyperspectral Imaging." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 796–806. http://dx.doi.org/10.31142/ijtsrd10983.

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2

Müller-Rowold, M., and R. Reulke. "HYPERSPECTRAL PANORAMIC IMAGING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 323–28. http://dx.doi.org/10.5194/isprs-archives-xlii-1-323-2018.

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<p><strong>Abstract.</strong> Hyperspectral instruments are designed for the characterisation of planetary surfaces, oceans and the atmosphere. At the moment there are a number of aircraft systems and planned space missions. Examples for this are the hyperspectral missions for Earth remote sensing (EnMAP) and also for deep space and planetary missions (Mercury mission Bepi Colombo).</p><p>There are basically two options for a hyperspectral system: Snapshot systems and scanning systems. This paper investigates a scanning hyperspectral push-broom systems. In most systems the input aperture is a long slit whose image is dispersed across a 2-D detector array, so that all points along a line in the scene are sampled simultaneously. To fill out the spatial dimension orthogonal to the slit, the scene is scanned across the entrance aperture. An ideal low cost hyperspectral scanning device analogue to push broom scanner is a 2D-detector with variable spectral filters, each filter being arranged perpendicular to the direction of flight.</p><p>The biggest challenge is the mapping of the images of the individual spectral channels to each other (co-registration). The solution of the problem is the prerequisite for the use of this kind of hyperspectral cameras e.g. on board of an aircraft. Therefore, an investigation should focus on the procedure of data acquisition, correction and registration. In addition, an example showing the advantages of a possible application is explained.</p>

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3

Bhargava, Rohit, and Kianoush Falahkheirkhah. "Enhancing hyperspectral imaging." Nature Machine Intelligence 3, no. 4 (April 2021): 279–80. http://dx.doi.org/10.1038/s42256-021-00336-9.

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4

Rui Zhou, Rui Zhou, Manping Ye Manping Ye, and Huacai Chen Huacai Chen. "Apple bruise detect with hyperspectral imaging technique." Chinese Optics Letters 12, s1 (2014): S11101–311103. http://dx.doi.org/10.3788/col201412.s11101.

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5

Changsheng Liu, Changsheng Liu, Zhimin Han Zhimin Han, and Tianyu Xie Tianyu Xie. "Hyperspectral high-dynamic-range endoscopic mucosal imaging." Chinese Optics Letters 13, no. 7 (2015): 071701–71705. http://dx.doi.org/10.3788/col201513.071701.

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6

Lu, Bing, Phuong D. Dao, Jiangui Liu, Yuhong He, and Jiali Shang. "Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture." Remote Sensing 12, no. 16 (August 18, 2020): 2659. http://dx.doi.org/10.3390/rs12162659.

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Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented.

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Wang, Zhixin, Peng Xu, Bohan Liu, Yankun Cao, Zhi Liu, and Zhaojun Liu. "Hyperspectral imaging for underwater object detection." Sensor Review 41, no. 2 (April 5, 2021): 176–91. http://dx.doi.org/10.1108/sr-07-2020-0165.

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Purpose This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed. Design/methodology/approach First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions. Findings Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future. Originality/value This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

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8

Chang, Chein-I., Meiping Song, Junping Zhang, and Chao-Cheng Wu. "Editorial for Special Issue “Hyperspectral Imaging and Applications”." Remote Sensing 11, no. 17 (August 27, 2019): 2012. http://dx.doi.org/10.3390/rs11172012.

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Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue “Hyperspectral Imaging and Applications” is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification, Band Selection, Data Fusion, Applications.

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9

Zou, Chunbo, Jianfeng Yang, Dengshan Wu, Qiang Zhao, Yuquan Gan, Di Fu, Fanchao Yang, Hong Liu, Qinglan Bai, and Bingliang Hu. "Design and Test of Portable Hyperspectral Imaging Spectrometer." Journal of Sensors 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7692491.

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We design and implement a portable hyperspectral imaging spectrometer, which has high spectral resolution, high spatial resolution, small volume, and low weight. The flight test has been conducted, and the hyperspectral images are acquired successfully. To achieve high performance, small volume, and regular appearance, an improved Dyson structure is designed and used in the hyperspectral imaging spectrometer. The hyperspectral imaging spectrometer is suitable for the small platform such as CubeSat and UAV (unmanned aerial vehicle), and it is also convenient to use for hyperspectral imaging acquiring in the laboratory and the field.

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10

Stuart, Mary B., Andrew J. S. McGonigle, Matthew Davies, Matthew J. Hobbs, Nicholas A. Boone, Leigh R. Stanger, Chengxi Zhu, Tom D. Pering, and Jon R. Willmott. "Low-Cost Hyperspectral Imaging with A Smartphone." Journal of Imaging 7, no. 8 (August 5, 2021): 136. http://dx.doi.org/10.3390/jimaging7080136.

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Recent advances in smartphone technologies have opened the door to the development of accessible, highly portable sensing tools capable of accurate and reliable data collection in a range of environmental settings. In this article, we introduce a low-cost smartphone-based hyperspectral imaging system that can convert a standard smartphone camera into a visible wavelength hyperspectral sensor for ca. £100. To the best of our knowledge, this represents the first smartphone capable of hyperspectral data collection without the need for extensive post processing. The Hyperspectral Smartphone’s abilities are tested in a variety of environmental applications and its capabilities directly compared to the laboratory-based analogue from our previous research, as well as the wider existing literature. The Hyperspectral Smartphone is capable of accurate, laboratory- and field-based hyperspectral data collection, demonstrating the significant promise of both this device and smartphone-based hyperspectral imaging as a whole.

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11

Yang, Shuowen, Xiang Yan, Hanlin Qin, Qingjie Zeng, Yi Liang, Henry Arguello, and Xin Yuan. "Mid-Infrared Compressive Hyperspectral Imaging." Remote Sensing 13, no. 4 (February 17, 2021): 741. http://dx.doi.org/10.3390/rs13040741.

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Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to the mid-infrared spectral range. In this paper, we report a novel mid-infrared compressive HSI system to extend the application domain of mid-infrared digital micromirror device (MIR-DMD). In our system, a modified MIR-DMD is combined with an off-the-shelf infrared spectroradiometer to capture the spatial modulated and compressed measurements at different spectral channels. Following this, a dual-stage image reconstruction method is developed to recover infrared hyperspectral images from these measurements. In addition, a measurement without any coding is used as the side information to aid the reconstruction to enhance the reconstruction quality of the infrared hyperspectral images. A proof-of-concept setup is built to capture the mid-infrared hyperspectral data of 64 pixels × 48 pixels × 100 spectral channels ranging from 3 to 5 μm, with the acquisition time within one minute. To the best of our knowledge, this is the first mid-infrared compressive hyperspectral imaging approach that could offer a less expensive alternative to conventional mid-infrared hyperspectral imaging systems.

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12

Klein, Marvin, Bernard Aalderink, Roberto Padoan, Gerrit De Bruin, and Ted Steemers. "Quantitative Hyperspectral Reflectance Imaging." Sensors 8, no. 9 (September 11, 2008): 5576–618. http://dx.doi.org/10.3390/s8095576.

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13

Goltz, Douglas, and Gregory Hill. "Hyperspectral Imaging of Daguerreotypes." Restaurator. International Journal for the Preservation of Library and Archival Material 33, no. 1 (January 2012): 1–16. http://dx.doi.org/10.1515/res-2012-0001.

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14

Daukantas, Patricia. "Hyperspectral Imaging Meets Biomedicine." Optics and Photonics News 31, no. 4 (April 1, 2020): 32. http://dx.doi.org/10.1364/opn.31.4.000032.

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15

Jex, Catherine, Ela Claridge, Andy Baker, and Claire Smith. "Hyperspectral imaging of speleothems." Quaternary International 187, no. 1 (August 2008): 5–14. http://dx.doi.org/10.1016/j.quaint.2007.05.011.

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16

Cassis, Lisa A., Aaron Urbas, and Robert A. Lodder. "Hyperspectral integrated computational imaging." Analytical and Bioanalytical Chemistry 382, no. 4 (February 2, 2005): 868–72. http://dx.doi.org/10.1007/s00216-004-2979-1.

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17

Kellicut, D. C., J. M. Weiswasser, S. Arora, J. E. Freeman, R. A. Lew, C. Shuman, J. R. Mansfield, and A. N. Sidawy. "Emerging Technology: Hyperspectral Imaging." Perspectives in Vascular Surgery and Endovascular Therapy 16, no. 1 (March 1, 2004): 53–57. http://dx.doi.org/10.1177/153100350401600114.

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18

Kehlet, Louis Martinus, Peter Tidemand-Lichtenberg, Jeppe Seidelin Dam, and Christian Pedersen. "Infrared upconversion hyperspectral imaging." Optics Letters 40, no. 6 (March 5, 2015): 938. http://dx.doi.org/10.1364/ol.40.000938.

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19

Hamid Muhammed, Hamed. "Affordable simultaneous hyperspectral imaging." Sensor Review 33, no. 3 (June 14, 2013): 257–66. http://dx.doi.org/10.1108/02602281311324717.

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20

Ross, Claire G., Carley Chwal, Jeffrey A. Beckstead, Roger W. Byard, and Neil E. I. Langlois. "Hyperspectral imaging of bruises." Pathology 46 (2014): S88. http://dx.doi.org/10.1097/01.pat.0000443642.14560.55.

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21

Liu, Wenhai, George Barbastathis, and Demetri Psaltis. "Volume Holographic Hyperspectral Imaging." Applied Optics 43, no. 18 (2004): 3581. http://dx.doi.org/10.1364/ao.43.003581.

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22

Chang, Chein-I., Meiping Song, Chunyan Yu, Yulei Wang, Haoyang Yu, Jiaojiao Li, Lin Wang, Hsiao-Chi Li, and Xiaorun Li. "Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”." Remote Sensing 14, no. 20 (October 13, 2022): 5111. http://dx.doi.org/10.3390/rs14205111.

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Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expanded from unmixing data samples and detecting targets at the subpixel scale to finding endmembers, which generally cannot be resolved by multispectral imaging. Accordingly, a wealth of new HSI research has been conducted and reported in the literature in recent years. The aim of this Special Issue “Advances in Hyperspectral Data Exploitation“ is to provide a forum for scholars and researchers to publish and share their research ideas and findings to facilitate the utility of hyperspectral imaging in data exploitation and other applications. With this in mind, this Special Issue accepted and published 19 papers in various areas, which can be organized into 9 categories, including I: Hyperspectral Image Classification, II: Hyperspectral Target Detection, III: Hyperspectral and Multispectral Fusion, IV: Mid-wave Infrared Hyperspectral Imaging, V: Hyperspectral Unmixing, VI: Hyperspectral Sensor Hardware Design, VII: Hyperspectral Reconstruction, VIII: Hyperspectral Visualization, and IX: Applications.

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23

Pallua, Johannes D., Andrea Brunner, Bernhard Zelger, Christian W. Huck, Michael Schirmer, Johannes Laimer, David Putzer, Martin Thaler, and Bettina Zelger. "New perspectives of hyperspectral imaging for clinical research." NIR news 32, no. 3-4 (June 2021): 5–13. http://dx.doi.org/10.1177/09603360211024971.

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New developments in instrumentation and data analysis have further improved the perspectives of hyperspectral imaging in clinical use. Thus, hyperspectral imaging can be considered as “Next Generation Imaging” for future clinical research. As a contactless, non-invasive method with short process times of just a few seconds, it quantifies predefined substance classes. Results of hyperspectral imaging may support the detection of carcinomas and the classification of different tissue structures as well as the assessment of tissue blood flow. Taken together, this method combines the principle of spectroscopy with imaging using conventional visual cameras. Compared to other optical imaging methods, hyperspectral imaging also analyses deeper layers of tissue.

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24

Jiang, Ying Lan, Ruo Yu Zhang, Jie Yu, Wan Chao Hu, and Zhang Tao Yin. "Applications of Visible and near-Infrared Hyperspectral Imaging for Non-Destructive Detection of the Agricultural Products." Advanced Materials Research 317-319 (August 2011): 909–14. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.909.

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Agricultural products quality which included intrinsic attribute and extrinsic characteristic, closely related to the health of consumer and the exported cost. Now, imaging (machine vision) and spectrum are two main nondestructive inspection technologies to be applied. Hyperspectral imaging, a new emerging technology developed for detecting quality of the food and agricultural products in recent years, combined techniques of conventional imaging and spectroscopy to obtain both spatial and spectral information from an objective simultaneously. This paper compared the advantage and disadvantage of imaging, spectrum and hyperspectral imaging technique, and provided a description to basic principle, feature of hyperspectral imaging system and calibration of hyperspectral reflectance images. In addition, the recent advances for the application of hyperspectral imaging to agricultural products quality inspection were reviewed in other countries and China.

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25

Pechlivani, Eleftheria Maria, Athanasios Papadimitriou, Sotirios Pemas, Nikolaos Giakoumoglou, and Dimitrios Tzovaras. "Low-Cost Hyperspectral Imaging Device for Portable Remote Sensing." Instruments 7, no. 4 (October 19, 2023): 32. http://dx.doi.org/10.3390/instruments7040032.

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Hyperspectral imaging has revolutionized various scientific fields by enabling a detailed analysis of objects and materials based on their spectral signatures. However, the high cost and complexity of commercial hyperspectral camera systems limit their accessibility to researchers and professionals. In this paper, a do-it-yourself (DIY) hyperspectral camera device that offers a cost-effective and user-friendly alternative to hyperspectral imaging is presented. The proposed device leverages off-the-shelf components, commercially available hardware parts, open-source software, and novel calibration techniques to capture and process hyperspectral imaging data. The design considerations, hardware components, and construction process are discussed, providing a comprehensive guide for building the device. Furthermore, the performance of the DIY hyperspectral camera is investigated through experimental evaluations with a multi-color 3D-printed box in order to validate its sensitivities to red, green, blue, orange and white colors.

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Zaman, Zainab, Saad Bin Ahmed, and Muhammad Imran Malik. "Analysis of Hyperspectral Data to Develop an Approach for Document Images." Sensors 23, no. 15 (August 1, 2023): 6845. http://dx.doi.org/10.3390/s23156845.

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Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain.

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Liu, Bohan, Shaojie Men, Zhongjun Ding, Dewei Li, Zhigang Zhao, Jiahao He, Haochen Ju, Mengling Shen, Qiuyuan Yu, and Zhaojun Liu. "Underwater Hyperspectral Imaging System with Liquid Lenses." Remote Sensing 15, no. 3 (January 17, 2023): 544. http://dx.doi.org/10.3390/rs15030544.

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The underwater hyperspectral imager enables the detection and identification of targets on the seafloor by collecting high-resolution spectral images. The distance between the hyperspectral imager and the targets cannot be consistent in real operation by factors such as motion and fluctuating terrain, resulting in unfocused images and negative effects on the identification. In this paper, we developed a novel integrated underwater hyperspectral imaging system for deep sea surveys and proposed an autofocus strategy based on liquid lens focusing transfer. The calibration tests provided a clear focus result for hyperspectral transects and a global spectral resolution of less than 7 nm in spectral range from 400 to 800 nm. The prototype was used to obtain spectrum and image information of manganese nodules and four other rocks in a laboratory environment. The classification of the five kinds of minerals was successfully realized by using a support vector machine. We tested the UHI prototype in the deep sea and observed a Psychropotidae specimen on the sediment from the in situ hyperspectral images. The results show that the prototype developed here can accurately and stably obtain hyperspectral data and has potential applications for in situ deep-sea exploration.

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28

Channing, Georgia. "Spectral DefocusCam: Compressive Hyperspectral Imaging from Defocus Measurements." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13128–29. http://dx.doi.org/10.1609/aaai.v36i11.21700.

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Hyperspectral imaging is used for a wide range of tasks from medical diagnostics to crop monitoring, but traditional imagers are prohibitively expensive for widespread use. This research strives to democratize hyperspectral imaging by using machine learning to reconstruct hyperspectral volumes from snapshot imagers. I propose a tunable lens with varying amounts of defocus paired with 31-channel spectral filter array mounted on a CMOS camera. These images are then fed into a reconstruction network that aims to recover the full 31-channel hyperspectral volume from a few encoded images with different amounts of defocus.

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Bachmann, Charles, Rehman Eon, Christopher Lapszynski, Gregory Badura, Anthony Vodacek, Matthew Hoffman, Donald Mckeown, et al. "A Low-Rate Video Approach to Hyperspectral Imaging of Dynamic Scenes." Journal of Imaging 5, no. 1 (December 31, 2018): 6. http://dx.doi.org/10.3390/jimaging5010006.

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The increased sensitivity of modern hyperspectral line-scanning systems has led to the development of imaging systems that can acquire each line of hyperspectral pixels at very high data rates (in the 200–400 Hz range). These data acquisition rates present an opportunity to acquire full hyperspectral scenes at rapid rates, enabling the use of traditional push-broom imaging systems as low-rate video hyperspectral imaging systems. This paper provides an overview of the design of an integrated system that produces low-rate video hyperspectral image sequences by merging a hyperspectral line scanner, operating in the visible and near infra-red, with a high-speed pan-tilt system and an integrated IMU-GPS that provides system pointing. The integrated unit is operated from atop a telescopic mast, which also allows imaging of the same surface area or objects from multiple view zenith directions, useful for bi-directional reflectance data acquisition and analysis. The telescopic mast platform also enables stereo hyperspectral image acquisition, and therefore, the ability to construct a digital elevation model of the surface. Imaging near the shoreline in a coastal setting, we provide an example of hyperspectral imagery time series acquired during a field experiment in July 2017 with our integrated system, which produced hyperspectral image sequences with 371 spectral bands, spatial dimensions of 1600 × 212, and 16 bits per pixel, every 0.67 s. A second example times series acquired during a rooftop experiment conducted on the Rochester Institute of Technology campus in August 2017 illustrates a second application, moving vehicle imaging, with 371 spectral bands, 16 bit dynamic range, and 1600 × 300 spatial dimensions every second.

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Wang, Zhongliang, and Hua Xiao. "Distributed Compressed Hyperspectral Sensing Imaging Based on Spectral Unmixing." Sensors 20, no. 8 (April 17, 2020): 2305. http://dx.doi.org/10.3390/s20082305.

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The huge volume of hyperspectral imagery demands enormous computational resources, storage memory, and bandwidth between the sensor and the ground stations. Compressed sensing theory has great potential to reduce the enormous cost of hyperspectral imagery by only collecting a few compressed measurements on the onboard imaging system. Inspired by distributed source coding, in this paper, a distributed compressed sensing framework of hyperspectral imagery is proposed. Similar to distributed compressed video sensing, spatial-spectral hyperspectral imagery is separated into key-band and compressed-sensing-band with different sampling rates during collecting data of proposed framework. However, unlike distributed compressed video sensing using side information for reconstruction, the widely used spectral unmixing method is employed for the recovery of hyperspectral imagery. First, endmembers are extracted from the compressed-sensing-band. Then, the endmembers of the key-band are predicted by interpolation method and abundance estimation is achieved by exploiting sparse penalty. Finally, the original hyperspectral imagery is recovered by linear mixing model. Extensive experimental results on multiple real hyperspectral datasets demonstrate that the proposed method can effectively recover the original data. The reconstruction peak signal-to-noise ratio of the proposed framework surpasses other state-of-the-art methods.

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Vairavan, C., B. M. Kamble, A. G. Durgude, Snehal R. Ingle, and K. Pugazenthi. "Hyperspectral Imaging of Soil and Crop: A Review." Journal of Experimental Agriculture International 46, no. 1 (January 17, 2024): 48–61. http://dx.doi.org/10.9734/jeai/2024/v46i12290.

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The remote sensing is one of the precision technologies, can be used to monitor and assess the target area or object such as soil, crop, and water. Hyperspectral imaging (HSI), also known as imaging spectrometry or hyperspectral remote sensing, is a combined technique of spectroscopy and imaging system for sensing spectral information of an area or object. It involves capturing images of an object using multiple distinct optical bands that cover a wide range of the electromagnetic spectrum (350-2500 nm). The hyperspectral bands are continuous, narrow, and contagious and contain hundreds and thousands of numbers. Hyperspectral remote sensing is particularly valuable for gathering precise and up-to-date information necessary for agricultural planning and precision farming. HSI technology is the employment of hyperspectral sensors aids in analyzing soil physical (bulk density, texture, water content), chemical (pH, EC, SOC, and macro and micro nutrients), biological (SOM) properties and helps to categorize different crop varieties, identify pests and diseases, and assess crop yield and water stress in plants. The spectral reflectance of soil is affected by its properties such as mineral composition (Fe oxides), organic matter, soil moisture, and texture. For example, the spectral reflectance will be more if soil has less organic matter. The chemical bonds of soil molecules interact with the electromagnetic spectrum, and produce distinct pattern of reflectance. But the data collected from hyperspectral imaging are required big storage due to its large amount of data and finding the most appropriate hyperspectral image classification algorithm is a challenging task. So, these problems should be solved in future and national soil spectral library is needed for calibration of models which helps for efficient use of hyperspectral imaging technology.

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32

Yule, I., R. Pullanagari, M. Irwin, P. Mcveagh, G. Kereszturi, M. White, and M. Manning. "Mapping nutrient concentration in pasture using hyperspectral imaging." Journal of New Zealand Grasslands 77 (January 1, 2015): 47–50. http://dx.doi.org/10.33584/jnzg.2015.77.482.

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Hyperspectral sensing using ground-based equipment has been demonstrated to be successful in determining pasture nutrient content (N, P, K, S) and parameters such as dry matter content and metabolisable energy. This technology needed to be up-scaled so that large areas could be rapidly covered with adequate spatial resolution. This paper describes work which demonstrates a progression from hyperspectral sensing to hyperspectral imaging which utilises the visible, near infrared and short wave infrared parts of the electromagnetic spectrum. Large scale calibration and validation field trials were conducted at the same time as hyperspectral imaging was completed. These trials demonstrate the feasibility of producing information, in detailed map form, on pasture nutrient concentration and other parameters to inform fertiliser placement decisions as well as other farm management tasks. The technique effectively produces a forage analysis for every square meter of any farm. Keywords: hyperspectral imaging, precision agriculture.

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33

Juntunen, Cory, Isabel M. Woller, and Yongjin Sung. "Hyperspectral Three-Dimensional Fluorescence Imaging Using Snapshot Optical Tomography." Sensors 21, no. 11 (May 24, 2021): 3652. http://dx.doi.org/10.3390/s21113652.

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Hyperspectral three-dimensional (3D) imaging can provide both 3D structural and functional information of a specimen. The imaging throughput is typically very low due to the requirement of scanning mechanisms for different depths and wavelengths. Here we demonstrate hyperspectral 3D imaging using Snapshot projection optical tomography (SPOT) and Fourier-transform spectroscopy (FTS). SPOT allows us to instantaneously acquire the projection images corresponding to different viewing angles, while FTS allows us to perform hyperspectral imaging at high spectral resolution. Using fluorescent beads and sunflower pollens, we demonstrate the imaging performance of the developed system.

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34

Borana, S. L., S. K. Yadav, and R. T. Paturkar. "DISCRIMINATION AND CHARACTERIZATION OF PROMINENT DESERTIC VEGETATIONS USING HYPERSPECTRAL IMAGING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 363–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-363-2019.

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<p><strong>Abstract.</strong> Imaging Hyperspectral data are advent as potential solutions in modeling, discrimination and mapping of vegetation species. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, chlorophyll, and leaf nutrient concentration. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and combinations of suitable spectral band. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. In this study, a ground-based hyperspectral imaging data (AISA VNIR 400&ndash;960&thinsp;nm, Spectral Resolution @ 2.5&thinsp;nm) was used for spectral vegetation discrimination and characterization of natural desertic tree species. This study assessed the utility of hyperspectral imagery of 240 narrow bands in discrimination and classification of desert tree species in Jodhpur region using ENVI software. Vegetation indices derived from hyperspectral images used in the Analysis for tree species classification discrimination study. Prominent occurring two desertic tree species, viz., Neem and Babul in Jodhpur region could be effectively discriminated. Study demonstrated the potential utility of narrow spectral bands of Hyperspectral Imaging data in discriminating vegetation species in a desertic terrain.</p>

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Zulfiqar, Maheen, Muhammad Ahmad, Ahmed Sohaib, Manuel Mazzara, and Salvatore Distefano. "Hyperspectral Imaging for Bloodstain Identification." Sensors 21, no. 9 (April 27, 2021): 3045. http://dx.doi.org/10.3390/s21093045.

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Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.

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Papke, Kai, and Christoph Hornberger. "On model-based hyperspectral imaging." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 789–92. http://dx.doi.org/10.1515/cdbme-2022-1201.

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Abstract Several contributions are presented to aid the analysis of hyperspectral images by utilizing appropriate optical models. The algorithms are described by means of the perfusion of human skin and a corresponding two-layer tissue model. The particular role of the mean photon path length within the medium is discussed and two approaches to solve the inverse problem, i.e., the derivision of perfusion parameters from given spectra, are outlined.

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Selci, Stefano. "The Future of Hyperspectral Imaging." Journal of Imaging 5, no. 11 (October 25, 2019): 84. http://dx.doi.org/10.3390/jimaging5110084.

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The Special Issue on hyperspectral imaging (HSI), entitled “The Future of Hyperspectral Imaging”, has published 12 papers. Nine papers are related to specific current research and three more are review contributions: In both cases, the request is to propose those methods or instruments so as to show the future trends of HSI. Some contributions also update specific methodological or mathematical tools. In particular, the review papers address deep learning methods for HSI analysis, while HSI data compression is reviewed by using liquid crystals spectral multiplexing as well as DMD-based Raman spectroscopy. Specific topics explored by using data obtained by HSI include alert on the sprouting of potato tubers, the investigation on the stability of painting samples, the prediction of healing diabetic foot ulcers, and age determination of blood-stained fingerprints. Papers showing advances on more general topics include video approach for HSI dynamic scenes, localization of plant diseases, new methods for the lossless compression of HSI data, the fusing of multiple multiband images, and mixed modes of laser HSI imaging for sorting and quality controls.

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Li, Jiaqiong, Yi Zheng, Lingling Liu, and Beiwen Li. "4D line-scan hyperspectral imaging." Optics Express 29, no. 21 (October 8, 2021): 34835. http://dx.doi.org/10.1364/oe.441213.

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Yoon, Jonghee. "Hyperspectral Imaging for Clinical Applications." BioChip Journal 16, no. 1 (January 4, 2022): 1–12. http://dx.doi.org/10.1007/s13206-021-00041-0.

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AbstractMeasuring morphological and biochemical features of tissue is crucial for disease diagnosis and surgical guidance, providing clinically significant information related to pathophysiology. Hyperspectral imaging (HSI) techniques obtain both spatial and spectral features of tissue without labeling molecules such as fluorescent dyes, which provides rich information for improved disease diagnosis and treatment. Recent advances in HSI systems have demonstrated its potential for clinical applications, especially in disease diagnosis and image-guided surgery. This review summarizes the basic principle of HSI and optical systems, deep-learning-based image analysis, and clinical applications of HSI to provide insight into this rapidly growing field of research. In addition, the challenges facing the clinical implementation of HSI techniques are discussed.

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Johnson, William R., Daniel W. Wilson, Wolfgang Fink, Mark Humayun, and Greg Bearman. "Snapshot hyperspectral imaging in ophthalmology." Journal of Biomedical Optics 12, no. 1 (2007): 014036. http://dx.doi.org/10.1117/1.2434950.

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Lin, Xing, Gordon Wetzstein, Yebin Liu, and Qionghai Dai. "Dual-coded compressive hyperspectral imaging." Optics Letters 39, no. 7 (March 26, 2014): 2044. http://dx.doi.org/10.1364/ol.39.002044.

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Offerhaus, Herman L., Sarah E. Bohndiek, and Andrew R. Harvey. "Hyperspectral imaging in biomedical applications." Journal of Optics 21, no. 1 (December 10, 2018): 010202. http://dx.doi.org/10.1088/2040-8986/aaf2a0.

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Reshef, Edith R., John B. Miller, and Demetrios G. Vavvas. "Hyperspectral Imaging of the Retina." International Ophthalmology Clinics 60, no. 1 (2020): 85–96. http://dx.doi.org/10.1097/iio.0000000000000293.

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Gaudi, Sudeep, Rebecca Meyer, Jayshree Ranka, James C. Granahan, Steven A. Israel, Theodore R. Yachik, and Drazen M. Jukic. "Hyperspectral Imaging of Melanocytic Lesions." American Journal of Dermatopathology 36, no. 2 (February 2014): 131–36. http://dx.doi.org/10.1097/dad.0b013e31829bad10.

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Lu, Guolan, and Baowei Fei. "Medical hyperspectral imaging: a review." Journal of Biomedical Optics 19, no. 1 (January 20, 2014): 010901. http://dx.doi.org/10.1117/1.jbo.19.1.010901.

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Favreau, Peter F., Clarissa Hernandez, Tiffany Heaster, Diego F. Alvarez, Thomas C. Rich, Prashant Prabhat, and Silas J. Leavesley. "Excitation-scanning hyperspectral imaging microscope." Journal of Biomedical Optics 19, no. 4 (April 11, 2014): 046010. http://dx.doi.org/10.1117/1.jbo.19.4.046010.

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Martín-Mateos, Pedro, Farid Ullah Khan, and Oscar Elías Bonilla-Manrique. "Direct hyperspectral dual-comb imaging." Optica 7, no. 3 (February 27, 2020): 199. http://dx.doi.org/10.1364/optica.382887.

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Mary A, Pramila, and Thippeswamy G. "Leaf Quality :Hyperspectral Imaging Technology." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 693. http://dx.doi.org/10.14419/ijet.v7i3.12.16456.

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India is a rural nation 70% of the populace relies upon horticulture. Farmers are the backbone of our nation.70% of the economic growth depends on agriculture. The major food crops of India are wheat, corn, rice, barley, sorghum. In this project the concentration is on paddy as it is a main food crop of our state. This project helps us to find whether the leaf is diseased or not and also helps us to find the type of disease in paddy leaf. The agribusiness research of programmed leaf sickness recognition is fundamental research point as it might demonstrate benefits in checking substantial fields of products, and subsequently naturally recognize manifestations of illness when they show up on plant takes off. Digital image processing Advanced is a procedure utilized for improvement of the picture. To enhance farming items programmed recognition of side effects is useful.

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Park, Bosoon. "Future Trends in Hyperspectral Imaging." NIR news 27, no. 1 (February 2016): 35–38. http://dx.doi.org/10.1255/nirn.1583.

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Bouillard, Jean-Sebastien G., Wayne Dickson, Gregory A. Wurtz, and Anatoly V. Zayats. "Near-Field Hyperspectral Optical Imaging." ChemPhysChem 15, no. 4 (February 13, 2014): 619–29. http://dx.doi.org/10.1002/cphc.201300826.

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