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1
Lefèvre-Fonollosa, Marie-José, Sylvain Michel, and Steven Hosford. "HYPXIM — An innovative spectroimager for science, security, and defence requirements." Revue Française de Photogrammétrie et de Télédétection, no. 200 (April 19, 2014): 20–27. http://dx.doi.org/10.52638/rfpt.2012.58.
Повний текст джерелаАнотація:
Cet article présente un aperçu des applications et des besoins de données hyperspectrales recueillis par un groupe adhoc d'une vingtaine de scientifiques français et d'utilisateurs Civil et de la Défense (i.a. dual). Ce groupe connu sous l'acronyme GHS (Groupe de Synthèse en Hyperspectral) a défini les exigences techniques pour une mission spatiale de haute résolution en hyperspectral répondant aux besoins des thèmes suivants: la végétation naturelle et agricole, les écosystèmes aquatiques côtiers et lacustres, les géosciences, l'environnement urbain, l'atmosphère, la sécurité et la défense.La synthèse de ces exigences a permis de décrire les spécifications d'un satellite très innovant en terme de domaine spectral, de résolution spectrale, de rapport signal à bruit, de résolution spatiale, de fauchée et de répétitivité. HYPXIM est une mission hyperspectrale spatiale de nouvelle génération qui répond aux besoins d'une large communauté d'utilisateurs de données à haute résolution dans le monde.Les principaux points ont été étudiés dans la phase 0 (pré-phase A) menée par le CNES avec ses partenaires industriels (EADS-Astrium et Thales Alenia Space). Deux concepts de satellites ont été étudiés et comparés. Le premier, appelé HYPXIM-C, vise à obtenir le niveau de résolution le plus élevé possible (15 m) réalisable en utilisant une plateforme de microsatellite. Les objectifs du deuxième, appelé HYPXIM-P, sont d'atteindre une résolution spatiale supérieure d'un facteur deux en hyperspectral (7-8m), un canal panchromatique (2m) et de fournir une capacité en infrarouge hyperspectral (100 m) sur un mini satellite. La phase A HYPXIM a été récemment décidée. Elle démarre en 2012 en se concentrant sur le concept le plus performant. Le défi pour la mission HYPXIM qui a été sélectionnée est de concevoir un spectroimageur à haute résolution spatiale, sur un mini-satellite agile à moindre coût.Ces études préliminaires ouvrent des perspectives pour un lancement possible en 2020/21 en fonction du développement des technologies critiques.
2
Stuart, Mary B., Leigh R. Stanger, Matthew J. Hobbs, Tom D. Pering, Daniel Thio, Andrew J. S. McGonigle, and Jon R. Willmott. "Low-Cost Hyperspectral Imaging System: Design and Testing for Laboratory-Based Environmental Applications." Sensors 20, no. 11 (June 9, 2020): 3293. http://dx.doi.org/10.3390/s20113293.
Повний текст джерелаАнотація:
The recent surge in the development of low-cost, miniaturised technologies provides a significant opportunity to develop miniaturised hyperspectral imagers at a fraction of the cost of currently available commercial set-ups. This article introduces a low-cost laboratory-based hyperspectral imager developed using commercially available components. The imager is capable of quantitative and qualitative hyperspectral measurements, and it was tested in a variety of laboratory-based environmental applications where it demonstrated its ability to collect data that correlates well with existing datasets. In its current format, the imager is an accurate laboratory measurement tool, with significant potential for ongoing future developments. It represents an initial development in accessible hyperspectral technologies, providing a robust basis for future improvements.
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Honkavaara, E., T. Hakala, O. Nevalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, R. Näsi, R. Oliveira, and A. Tommaselli. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprsarchives-xli-b7-77-2016.
Повний текст джерелаАнотація:
Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.
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Honkavaara, E., T. Hakala, O. Nevalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, R. Näsi, R. Oliveira, and A. Tommaselli. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprs-archives-xli-b7-77-2016.
Повний текст джерелаАнотація:
Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.
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Zhang, Ning, Guijun Yang, Yuchun Pan, Xiaodong Yang, Liping Chen, and Chunjiang Zhao. "A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades." Remote Sensing 12, no. 19 (September 29, 2020): 3188. http://dx.doi.org/10.3390/rs12193188.
Повний текст джерелаАнотація:
The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.
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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.
Повний текст джерелаАнотація:
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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LeVan, Paul D. "Space-based hyperspectral technologies for the thermal infrared." Optical Engineering 52, no. 6 (March 4, 2013): 061311. http://dx.doi.org/10.1117/1.oe.52.6.061311.
Повний текст джерела
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Allik, Toomas H., Roberta E. Dixon, Lenard V. Ramboyong, Mark Roberts, Thomas J. Soyka, George Trifon, and Lori Medley. "Novel Electro-Optic Imaging Technologies for Day/Night Oil Spill Detection." International Oil Spill Conference Proceedings 2014, no. 1 (May 1, 2014): 299609. http://dx.doi.org/10.7901/2169-3358-2014-1-299609.1.
Повний текст джерелаАнотація:
Joint program between the U.S. Departments of the Interior and Defense to bring knowledge, expertise and military, low-light level and hyperspectral imaging technologies to remote oil spill detection. Program emphasis is to determine remote infrared imaging techniques for the quantification of oil spill thickness. Spectral characteristics of various crude oils in the SWIR (1–2 microns), MWIR (3–5 microns) and LWIR (8–12 microns) were measured. Analysis of laboratory data and Deepwater Horizon hyperspectral imagery showed the utility of the SWIR region to detect crude oil and emulsions. We have evaluated two SWIR wavelengths (1200 nm and 1250 nm) for thickness assessment. An infrared, 3-color imager is discussed along with field tests at the BSEE's Ohmsett test facility.
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Hu, B., J. Li, J. Wang, and B. Hall. "The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2 (November 11, 2014): 213–19. http://dx.doi.org/10.5194/isprsarchives-xl-2-213-2014.
Повний текст джерелаАнотація:
The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.
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Wu, Zebin, Jinping Gu, Yonglong Li, Fu Xiao, Jin Sun, and Zhihui Wei. "Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3252148.
Повний текст джерелаАнотація:
Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS), and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method.
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Mahlein, A. K., M. T. Kuska, J. Behmann, G. Polder, and A. Walter. "Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art." Annual Review of Phytopathology 56, no. 1 (August 25, 2018): 535–58. http://dx.doi.org/10.1146/annurev-phyto-080417-050100.
Повний текст джерелаАнотація:
Plant disease detection represents a tremendous challenge for research and practical applications. Visual assessment by human raters is time-consuming, expensive, and error prone. Disease rating and plant protection need new and innovative techniques to address forthcoming challenges and trends in agricultural production that require more precision than ever before. Within this context, hyperspectral sensors and imaging techniques—intrinsically tied to efficient data analysis approaches—have shown an enormous potential to provide new insights into plant-pathogen interactions and for the detection of plant diseases. This article provides an overview of hyperspectral sensors and imaging technologies for assessing compatible and incompatible plant-pathogen interactions. Within the progress of digital technologies, the vision, which is increasingly discussed in the society and industry, includes smart and intuitive solutions for assessing plant features in plant phenotyping or for making decisions on plant protection measures in the context of precision agriculture.
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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.
Повний текст джерелаАнотація:
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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Pascucci, Simone, Stefano Pignatti, Raffaele Casa, Roshanak Darvishzadeh, and Wenjiang Huang. "Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”." Remote Sensing 12, no. 21 (November 9, 2020): 3665. http://dx.doi.org/10.3390/rs12213665.
Повний текст джерелаАнотація:
The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.
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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.
Повний текст джерелаАнотація:
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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Awad, Mohamad M. "HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS." SWS Journal of EARTH AND PLANETARY SCIENCES 1, no. 1 (June 1, 2019): 25–37. http://dx.doi.org/10.35603/eps2019/issue1.03.
Повний текст джерелаАнотація:
In agriculture sector there is need for cheap, fast, and accurate data and technologies to help decision makers to find solutions for many agricultural problems. Many solutions depend significantly on the accuracy and efficiency of the crop mapping and crop yield estimation processes. High resolution spectral remote sensing can improve substantially crop mapping by reducing similarities between different crop types which has similar ecological conditions. This paper presents a new approach of combining a new tool, hyperspectral images and technologies to enhance crop mapping. The tool includes spectral signatures database for the major crops in the Eastern Mediterranean Basin and other important metadata and processing functions. To prove the efficiency of the new approach, major crops such as “winter wheat” and “spring potato” are mapped using the spectral signatures database in the new tool, three different supervised algorithms, and CHRIS-Proba hyperspectral satellite images. The evaluation of the results showed that deploying different hyperspectral data and technologies can improve crop mapping. The improvements can be noticed with the increase of the accuracy to more than 86% with the use of the supervised algorithm Spectral Angle Mapper (SAM).
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Datta, Debaleena, Pradeep Kumar Mallick, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Jana Shafi, and Jaeyoung Choi. "Hyperspectral Image Classification: Potentials, Challenges, and Future Directions." Computational Intelligence and Neuroscience 2022 (April 28, 2022): 1–36. http://dx.doi.org/10.1155/2022/3854635.
Повний текст джерелаАнотація:
Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies’ contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.
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Xu, Jitong, Fang Wang, Zhe Zhang, Yuhang Guo, Yufeng Liu, and Xiaofeng Ning. "Analysis of Greenness Value and Photosynthetic Rate of Tomato Leaves Based on Spectral Technologies." Horticulturae 8, no. 9 (September 12, 2022): 837. http://dx.doi.org/10.3390/horticulturae8090837.
Повний текст джерелаАнотація:
Tomatoes, a major vegetable crop, are not only delicious but can also prevent cancer and lower blood pressure. However, they are easily infected with diseases during the growth process, so it is of great significance to find a technology for nondestructive testing of the tomato growth state. In this study, partial least squares regression (PLSR) was used to establish a prediction model of the tomato leaf greenness value and photosynthetic rate based on laser-induced fluorescence spectroscopy and a hyperspectral imaging system. The results showed that the best preprocessing method for the fluorescence spectral model was SD+SNV, and the best methods for the hyperspectral model were FD+SNV and FD+MSC. The results for the prediction of the photosynthetic rate based on the fluorescence spectral and hyperspectral models were as follows: the coefficient of determination (R2) values were 0.9982 and 0.9739, respectively, and the root-mean-square error of prediction (RMSEP) values were 0.2781 and 0.3374, respectively. When measuring greenness, the R2 values were 0.9816 and 0.9595, and the RMSEP values were 0.1696 and 0.4032, respectively. The experimental results showed that the model based on the fluorescence spectrum had higher accuracy and lower deviation in the detection and prediction of the tomato growth state; these results provide a specific method and reference for subsequent research.
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Proshkin, Yuriy A. "Computer Vision and Spectral Analysis Technologies for Non-Invasive Plant Studying." Elektrotekhnologii i elektrooborudovanie v APK 67, no. 2 (June 24, 2020): 107–14. http://dx.doi.org/10.22314/2658-4859-2020-67-2-107-114.
Повний текст джерелаАнотація:
Computer vision and spectral analysis of digital images are technologies that allow the use of automated and robotic systems for non-invasive plant studying, production and harvesting of agricultural products, phenotyping and selection of new plant species. (Research purpose) The research purpose is in analyzing the application of modern digital non-invasive methods of plant research using computer (technical) vision and prospects for their implementation. (Materials and methods) Authors have reviewed the works on the use of non-invasive methods for obtaining information about the state of plants. The article presents classification and analyze of the collected materials according to the criteria for collecting and analyzing digital data, the scope of application and prospects for implementation. Authors used the methods of a systematic approach to the research problem. (Results and discussion) The article presents the main directions of using computer vision systems and digital image analysis. The use of computer vision technologies in plant phenotyping and selection reduces the labor cost of research, allowing the formation of digital databases with a clear structure and classification by morphological features. It was found that the introduction of neural networks in the process of digital image processing increases the accuracy of plant recognition up to 99.9 percent, and infectious diseases up to 80 percent on average. (Conclusions) The article shows that in studies using hyperspectral optical cameras and sensors are used cameras with an optical range from 400 to 1000 nanometers, and in rare cases, hyperspectral camera systems with a total coverage of the optical range from 350 to 2000 nanometers. These optical systems are mainly installed on unmanned aerial vehicles to determine vegetation indices, foci of infection and the fertility of agricultural fields. It was found that computer vision systems with hyperspectral cameras could be used in conjunction with fluorescent plant markers, which makes it possible to solve complex problems of crop recognition without involving computational resources.
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Näsi, R., N. Viljanen, J. Kaivosoja, T. Hakala, M. Pandžić, L. Markelin, and E. Honkavaara. "ASSESSMENT OF VARIOUS REMOTE SENSING TECHNOLOGIES IN BIOMASS AND NITROGEN CONTENT ESTIMATION USING AN AGRICULTURAL TEST FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (October 19, 2017): 137–41. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-137-2017.
Повний текст джерелаАнотація:
Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90 % for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers.
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Signoroni, Alberto, Mattia Savardi, Annalisa Baronio, and Sergio Benini. "Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review." Journal of Imaging 5, no. 5 (May 8, 2019): 52. http://dx.doi.org/10.3390/jimaging5050052.
Повний текст джерелаАнотація:
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
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Zhevlakov, A. P., V. G. Bespalov, O. B. Danilov, A. K. Zav’yalov, A. A. Il’inskiĭ, S. V. Kashcheev, L. A. Konopel’ko, A. A. Mak, A. S. Grishkanich, and V. V. Elizarov. "Raman hyperspectral technologies for remote probing of hydrocarbon geochemical fields." Journal of Optical Technology 87, no. 1 (January 1, 2020): 11. http://dx.doi.org/10.1364/jot.87.000011.
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Otkin, Jason A., Derek J. Posselt, Erik R. Olson, Hung-Lung Huang, James E. Davies, Jun Li, and Christopher S. Velden. "Mesoscale Numerical Weather Prediction Models Used in Support of Infrared Hyperspectral Measurement Simulation and Product Algorithm Development." Journal of Atmospheric and Oceanic Technology 24, no. 4 (April 1, 2007): 585–601. http://dx.doi.org/10.1175/jtech1994.1.
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Abstract A novel application of numerical weather prediction (NWP) models within an end-to-end processing system used to demonstrate advanced hyperspectral satellite technologies and instrument concepts is presented. As part of this system, sophisticated NWP models are used to generate simulated atmospheric profile datasets with fine horizontal and vertical resolution. The simulated datasets, which are treated as the “truth” atmosphere, are subsequently passed through a sophisticated forward radiative transfer model to generate simulated top-of-atmosphere (TOA) radiances across a broad spectral region. Atmospheric motion vectors and temperature and water vapor retrievals generated from the TOA radiances are then compared with the original model-simulated atmosphere to demonstrate the potential utility of future hyperspectral wind and retrieval algorithms. Representative examples of TOA radiances, atmospheric motion vectors, and temperature and water vapor retrievals are shown to illustrate the use of the simulated datasets. Case study results demonstrate that the numerical models are able to realistically simulate mesoscale cloud, temperature, and water vapor structures present in the real atmosphere. Because real hyperspectral radiance measurements with high spatial and temporal resolution are not available for large geographical domains, the simulated TOA radiance datasets are the only viable alternative that can be used to demonstrate the new hyperspectral technologies and capabilities. As such, sophisticated mesoscale models are critically important for the demonstration of the future end-to-end processing system.
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Tisserand, Stéphane. "VIS-NIR hyperspectral cameras." Photoniques, no. 110 (October 2021): 58–64. http://dx.doi.org/10.1051/photon/202111058.
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Hyperspectral and multispectral imaging can record a single scene across a range of spectral bands. The resulting three-dimensional dataset is called a "hypercube". A spectrum is available for each point of the image. This makes it possible to analyse, quantify or differentiate the elements and materials constituting the scene. This article presents the existing technologies on the market and their main characteristics in the VIS/NIR spectral domain (400-1000 nm). It then focuses on a specific multispectral technology called snapshot multispectral imaging, combining CMOS sensors and pixelated multispectral filters (filtering at the pixel level).
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Fodor, Margot, Julia Hofmann, Lukas Lanser, Giorgi Otarashvili, Marlene Pühringer, Theresa Hautz, Robert Sucher, and Stefan Schneeberger. "Hyperspectral Imaging and Machine Perfusion in Solid Organ Transplantation: Clinical Potentials of Combining Two Novel Technologies." Journal of Clinical Medicine 10, no. 17 (August 27, 2021): 3838. http://dx.doi.org/10.3390/jcm10173838.
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Organ transplantation survival rates have continued to improve over the last decades, mostly due to reduction of mortality early after transplantation. The advancement of the field is facilitating a liberalization of the access to organ transplantation with more patients with higher risk profile being added to the waiting list. At the same time, the persisting organ shortage fosters strategies to rescue organs of marginal donors. In this regard, hypothermic and normothermic machine perfusion are recognized as one of the most important developments in the modern era. Owing to these developments, novel non-invasive tools for the assessment of organ quality are on the horizon. Hyperspectral imaging represents a potentially suitable method capable of evaluating tissue morphology and organ perfusion prior to transplantation. Considering the changing environment, we here discuss the hypothetical combination of organ machine perfusion and hyperspectral imaging as a prospective feasibility concept in organ transplantation.
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Wozencraft, Jennifer, and David Millar. "Airborne Lidar and Integrated Technologies for Coastal Mapping and Nautical Charting." Marine Technology Society Journal 39, no. 3 (September 1, 2005): 27–35. http://dx.doi.org/10.4031/002533205787442440.
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The state-of-the-art in airborne coastal mapping and charting technology is the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) system. CHARTS is the U.S. Naval Oceanographic Office program name for an Optech, Inc. SHOALS 3000T20-E. CHARTS comprises a 3 kHz bathymetric lidar, a 20 kHz topographic lidar, a DuncanTech DT4000 high-resolution digital camera, and a Compact Airborne Spectrographic Imager(CASI)-1500. The integrated sensor suite has the capability to collect lidar bathymetry, lidar topography, RGB imagery, and hyperspectral imagery. Beyond these products, the diffuse attenuation coefficient and seafloor reflectance at multiple wavelengths may be estimated by combining information from the bathymetric lidar waveform and the hyperspectral imagery.The Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) specified development of the CHARTS system and currently manages its operations for Department of Defense customers. CHARTS data collection rate of 21 square nautical miles per survey hour enables rapid completion of large nautical charting work for the U.S. Naval Oceanographic Office. The U.S. Army Corps of Engineers National Coastal Mapping Program uses CHARTS to collect engineering scale data for the entire U.S. coastline. JALBTCX continues to lead development in the field of airborne lidar and integrated technologies for coastal mapping and charting. Future research efforts include mining the individual data sets collected by CHARTS for information beyond elevation, combining data sets to further identify physical and environmental characteristics of the coastal zone, and integrating additional complementary sensors with CHARTS.
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Jiang, Hongzhe, Wei Wang, Xinzhi Ni, Hong Zhuang, Seung-Chul Yoon, and Kurt C. Lawrence. "Recent advancement in near infrared spectroscopy and hyperspectral imaging techniques for quality and safety assessment of agricultural and food products in the China Agricultural University." NIR news 29, no. 8 (October 1, 2018): 19–23. http://dx.doi.org/10.1177/0960336018804755.
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Near infrared spectroscopy and hyperspectral imaging are fast-growing, rapid, powerful, and non-destructive optical technologies that can be used especially in quality and safety control of agro-food products. The Non-destructive Detecting Laboratory for Agricultural and Food Products in the College of Engineering, China Agricultural University in Beijing, China, has engaged in research on sensing and characterizing agro-food quality and safety attributes with the latest optical methods including near infrared spectroscopy and hyperspectral imaging for over five years. In this report, some of our latest research and developments through multidisciplinary international collaborations will be highlighted to demonstrate our contributions to this near infrared spectroscopy and hyperspectral imaging sensing area to improve non-destructive diagnosis and quality control of agricultural and food products.
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Xie, Yiting, Darren Plett, and Huajian Liu. "The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat." AgriEngineering 3, no. 4 (November 25, 2021): 924–41. http://dx.doi.org/10.3390/agriengineering3040058.
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Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the key tools to manage crown rot, because it is necessary to understand disease infection conditions, identify the severity of infection, and discover potential resistant varieties. However, screening crown rot is challenging as there are no clear visible symptoms on leaves at early growth stages. Hyperspectral imaging (HSI) technologies have been successfully used to better understand plant health and disease incidence, including light absorption rate, water and nutrient distribution, and disease classification. This suggests HSI imaging technologies may be used to detect crown rot at early growing stages, however, related studies are limited. This paper briefly describes the symptoms of crown rot disease and traditional screening methods with their limitations. It, then, reviews state-of-art imaging technologies for disease detection, from color imaging to hyperspectral imaging. In particular, this paper highlights the suitability of hyperspectral-based screening methods for crown rot disease. A hypothesis is presented that HSI can detect crown-rot-infected plants before clearly visible symptoms on leaves by sensing the changes of photosynthesis, water, and nutrients contents of plants. In addition, it describes our initial experiment to support the hypothesis and further research directions are described.
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Zhang, Haicheng, Beibei Jia, Yao Lu, Seung-Chul Yoon, Xinzhi Ni, Hong Zhuang, Xiaohuan Guo, Wenxin Le, and Wei Wang. "Detection of Aflatoxin B1 in Single Peanut Kernels by Combining Hyperspectral and Microscopic Imaging Technologies." Sensors 22, no. 13 (June 27, 2022): 4864. http://dx.doi.org/10.3390/s22134864.
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To study the dynamic changes of nutrient consumption and aflatoxin B1 (AFB1) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB1 contents from hyperspectral data ranging from 1000 to 2500 nm were developed and the results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with a support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance, with RC2 = 0.95 and RV2 = 0.93. Second, time-lapse microscopic images and spectroscopic data were captured and analyzed with scanning electron microscopy (SEM), transmission electron microscopy (TEM), and synchrotron radiation-Fourier transform infrared (SR-FTIR) microspectroscopy. The time-lapse data revealed the temporal patterns of nutrient loss and aflatoxin accumulation in peanut kernels. The combination of macro and micro imaging technologies proved to be an effective way to detect the interaction mechanism of toxigenic fungus infecting peanuts and to predict the accumulation of AFB1 quantitatively.
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Sulaiman, Nursyazyla, Nik Norasma Che’Ya, Muhammad Huzaifah Mohd Roslim, Abdul Shukor Juraimi, Nisfariza Mohd Noor, and Wan Fazilah Fazlil Ilahi. "The Application of Hyperspectral Remote Sensing Imagery (HRSI) for Weed Detection Analysis in Rice Fields: A Review." Applied Sciences 12, no. 5 (March 1, 2022): 2570. http://dx.doi.org/10.3390/app12052570.
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Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years to increase weed detection speed and accuracy, resolving the contradiction between the goals of enhancing soil health and achieving sufficient weed control for profitable farming. In recent years, a variety of platforms, such as satellites, airplanes, unmanned aerial vehicles (UAVs), and close-range platforms, have become more commonly available for gathering hyperspectral images with varying spatial, temporal, and spectral resolutions. Plants must be divided into crops and weeds based on their species for successful weed detection. Therefore, hyperspectral image categorization also has become popular since the development of hyperspectral image technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for weed detection and species separation. Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed for weeds discrimination analysis.
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Awad, Mohamad M. "Forest mapping: a comparison between hyperspectral and multispectral images and technologies." Journal of Forestry Research 29, no. 5 (November 9, 2017): 1395–405. http://dx.doi.org/10.1007/s11676-017-0528-y.
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Sousa, Joaquim J., Piero Toscano, Alessandro Matese, Salvatore Filippo Di Gennaro, Andrea Berton, Matteo Gatti, Stefano Poni, et al. "UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications." Sensors 22, no. 17 (August 31, 2022): 6574. http://dx.doi.org/10.3390/s22176574.
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Hyperspectral aerial imagery is becoming increasingly available due to both technology evolution and a somewhat affordable price tag. However, selecting a proper UAV + hyperspectral sensor combo to use in specific contexts is still challenging and lacks proper documental support. While selecting an UAV is more straightforward as it mostly relates with sensor compatibility, autonomy, reliability and cost, a hyperspectral sensor has much more to be considered. This note provides an assessment of two hyperspectral sensors (push-broom and snapshot) regarding practicality and suitability, within a precision viticulture context. The aim is to provide researchers, agronomists, winegrowers and UAV pilots with dependable data collection protocols and methods, enabling them to achieve faster processing techniques and helping to integrate multiple data sources. Furthermore, both the benefits and drawbacks of using each technology within a precision viticulture context are also highlighted. Hyperspectral sensors, UAVs, flight operations, and the processing methodology for each imaging type’ datasets are presented through a qualitative and quantitative analysis. For this purpose, four vineyards in two countries were selected as case studies. This supports the extrapolation of both advantages and issues related with the two types of hyperspectral sensors used, in different contexts. Sensors’ performance was compared through the evaluation of field operations complexity, processing time and qualitative accuracy of the results, namely the quality of the generated hyperspectral mosaics. The results shown an overall excellent geometrical quality, with no distortions or overlapping faults for both technologies, using the proposed mosaicking process and reconstruction. By resorting to the multi-site assessment, the qualitative and quantitative exchange of information throughout the UAV hyperspectral community is facilitated. In addition, all the major benefits and drawbacks of each hyperspectral sensor regarding its operation and data features are identified. Lastly, the operational complexity in the context of precision agriculture is also presented.
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Rodrigues, Sandra, and Joan Esterle. "Core scanner technologies: take everything without breaking." APPEA Journal 56, no. 2 (2016): 595. http://dx.doi.org/10.1071/aj15101.
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Modern core scanning technologies, such as hyperspectral CoreScan™ or X-ray fluorescence (XRF) Itrax, which allow data acquisition without the necessity of breaking the core for speciality analysis, are receiving increasing interest in coal and CSG industries in the past few years. Such technologies are able to characterise and evaluate mineral matter in greater detail than conventional sampling and analyses, producing mineral maps and mineral/elemental profiles throughout the core. Although mineralogical information is the main output from both techniques, CoreScan™ has the ability of producing organic profiles that allow the recognition of the different lithotypes in the coal based on the spectral reflectance as well as rank, which makes a potential technique for coal quality. On the other hand, XRF Itrax core scanner allies the chemical elemental profile, from major to trace elements, with an X-radiographic image, creating a dynamic duo between stony partings and coal, and within the coal between bright and dull lithotypes, through contrasting image properties. These emerging technologies will allow coal reservoirs to be analysed quickly and reliably without subsampling that could introduce bias from the user.
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Fischer, Christian, and Ioanna Kakoulli. "Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications." Studies in Conservation 51, sup1 (June 2006): 3–16. http://dx.doi.org/10.1179/sic.2006.51.supplement-1.3.
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Moroni, Monica, and Alessandro Mei. "Characterization and Separation of Traditional and Bio-Plastics by Hyperspectral Devices." Applied Sciences 10, no. 8 (April 17, 2020): 2800. http://dx.doi.org/10.3390/app10082800.
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Nowadays, bio-plastics can contaminate conventional plastics sent to recycling. Furthermore, the low volume of bio-plastics currently in use has discourage the development of new technologies for their identification and separation. Technologies based on hyperspectral data detection may be profitably employed to separate the bio-plastics from traditional ones and to increase the quality of recycled products. In fact, sensing devices make it possible to accomplish the essential requirement of a mechanical recycling technology, i.e., end products which comply with specific standards determined by industrial applications. This paper presents the results of the hyperspectral analysis conducted on two different plastic polymers (PolyEthylene Terephthalate and PolyStyrene) and one bio-based and biodegradable plastic material (PolyLactic Acid) in different phases of their life cycle (primary raw materials and urban waste). The reflectance analysis is focused on the near-infrared region (900–1700 nm) and data are detected with a linear-spectrometer apparatus and a spectroradiometer. A rapid and reliable identification of three investigated polymers is achieved by using simple two near-infrared wavelength operators employing key wavelengths.
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Moskovskiy, Maksim, Aleksander Lavrov, Anatoly Gulyaev, and Maksim Sidorov. "Trends in the development of non-invasive technologies for assessing quality of seeds." E3S Web of Conferences 285 (2021): 02014. http://dx.doi.org/10.1051/e3sconf/202128502014.
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The article presents studies on the use of non-invasive technologies to determine the quality of seeds of various crops. The authors analyzed patents on this topic. In particular, such laser and optical technologies as IR spectroscopy, hyperspectral spectroscopy, Raman spectroscopy, fluorescence, terahertz spectroscopy were investigated. The conducted patent research showed and confirmed the relevance of the work and the possibility of further implementation of the results into practice, mainly in the field of systems for laser-optical spectroscopy of seeds. Carrying out research on this topic will allow you to design and create new types of devices that will be in demand in many areas of modern agriculture.
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Riese, Felix M., Sina Keller, and Stefan Hinz. "Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data." Remote Sensing 12, no. 1 (December 18, 2019): 7. http://dx.doi.org/10.3390/rs12010007.
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Machine learning approaches are valuable methods in hyperspectral remote sensing, especially for the classification of land cover or for the regression of physical parameters. While the recording of hyperspectral data has become affordable with innovative technologies, the acquisition of reference data (ground truth) has remained expensive and time-consuming. There is a need for methodological approaches that can handle datasets with significantly more hyperspectral input data than reference data. We introduce the Supervised Self-organizing Maps (SuSi) framework, which can perform unsupervised, supervised and semi-supervised classification as well as regression on high-dimensional data. The methodology of the SuSi framework is presented and compared to other frameworks. Its different parts are evaluated on two hyperspectral datasets. The results of the evaluations can be summarized in four major findings: (1) The supervised and semi-Supervised Self-organizing Maps (SOM) outperform random forest in the regression of soil moisture. (2) In the classification of land cover, the supervised and semi-supervised SOM reveal great potential. (3) The unsupervised SOM is a valuable tool to understand the data. (4) The SuSi framework is versatile, flexible, and easy to use. The SuSi framework is provided as an open-source Python package on GitHub.
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Youcef Moudjib, Houari, Duan Haibin, Baochang Zhang, and Mohammed Salah Ahmed Ghaleb. "HSI-GCN: hyperspectral image classification algorithm based on Gabor convolutional networks." World Journal of Engineering 18, no. 4 (May 31, 2021): 590–95. http://dx.doi.org/10.1108/wje-09-2020-0460.
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Purpose Hyperspectral imaging (HSI) systems are becoming potent technologies for computer vision tasks due to the rich information they uncover, where each substance exhibits a distinct spectral distribution. Although the high spectral dimensionality of the data empowers feature learning, the joint spatial–spectral features have not been well explored yet. Gabor convolutional networks (GCNs) incorporate Gabor filters into a deep convolutional neural network (CNN) to extract discriminative features of different orientations and frequencies. To the best if the authors’ knowledge, this paper introduces the exploitation of GCNs for hyperspectral image classification (HSI-GCN) for the first time. HSI-GCN is able to extract deep joint spatial–spectral features more rapidly and accurately despite the shortage of training samples. The authors thoroughly evaluate the effectiveness of used method on different hyperspectral data sets, where promising results and high classification accuracy have been achieved compared to the previously proposed CNN-based and Gabor-based methods. Design/methodology/approach The authors have implemented the new algorithm of Gabor convolution network on the hyperspectral images for classification purposes. Findings Implementing the new GCN has shown unexpectable results with an excellent classification accuracy. Originality/value To the best of the authors’ knowledge, this work is the first one that implements this approach.
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Quan, Daying, Wei Feng, Gabriel Dauphin, Xiaofeng Wang, Wenjiang Huang, and Mengdao Xing. "A Novel Double Ensemble Algorithm for the Classification of Multi-Class Imbalanced Hyperspectral Data." Remote Sensing 14, no. 15 (August 5, 2022): 3765. http://dx.doi.org/10.3390/rs14153765.
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The class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based methods, and ensemble-based methods, have been proposed to solve the class imbalance problem. However, these methods suffer from the loss of useful information or from artificial noise, or result in overfitting. A novel double ensemble algorithm is proposed to deal with the multi-class imbalance problem of the hyperspectral image in this paper. This method first computes the feature importance values of the hyperspectral data via an ensemble model, then produces several balanced data sets based on oversampling and builds a number of classifiers. Finally, the classification results of these diversity classifiers are combined according to a specific ensemble rule. In the experiment, different data-handling methods and classification methods including random undersampling (RUS), random oversampling (ROS), Adaboost, Bagging, and random forest are compared with the proposed double random forest method. The experimental results on three imbalanced hyperspectral data sets demonstrate the effectiveness of the proposed algorithm.
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Schulz, J. "AIRBORNE TECHNOLOGIES FOR DISASTER MANAGEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 22, 2019): 387–93. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-387-2019.
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<p><strong>Abstract.</strong> Currently, satellite-based systems and UAVs are very popular in the investigation of natural disasters. Both systems have their justification and advantages &ndash; but one should not forget the airborne remote sensing technology. The presentation shows with three examples very clearly how airborne remote sensing is still making great progress and in many cases represents the optimal method of data acquisition.</p> <p>The airborne detection of forest damages (especially currently the bark beetle in spruce stands) can determine the pest attack using CIR aerial images in combination with ALS and hyperspectral systems &ndash; down to the individual tree. Large forest areas of 100 sqkm and more can be recorded from planes on one day (100 sqkm with 10cm GSD on one day).</p> <p>Flood events &ndash; such as on the Elbe in 2013 &ndash; were recorded by many satellites. However, many evaluations require highresolution data (GSD 10cm), e.g. to clarify insurance claims. Here the aircraft system, which was able to fly below the cloud cover and was constantly flying at the height level of the flood peak, proved to be unbeatable.</p> <p>The phenomenon of urban flash floods is one of the consequences of climate change. Cities are not in a position to cope with the water masses of extreme rain events and so are confronted with major damages. In Germany, a number of cities are already preparing to manage short-term but extreme water masses. The complicated hydrographic and hydraulic calculations and simulations require above all one thing &ndash; a precise data basis. This involves, for example, the height of kerbstones and the recording of every gully and every obstacle. Such city-wide data can only be collected effectively by photogrammetric analysis of aerial photography (GSD 5 to 10cm).</p>
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Pu, Ruiliang. "Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective." Journal of Remote Sensing 2021 (November 3, 2021): 1–26. http://dx.doi.org/10.34133/2021/9812624.
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Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.
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Liu, Ziwei, Jinbao Jiang, Mengquan Li, Deshuai Yuan, Cheng Nie, Yilin Sun, and Peng Zheng. "Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies." Foods 11, no. 8 (April 16, 2022): 1156. http://dx.doi.org/10.3390/foods11081156.
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Aflatoxins in moldy peanuts are seriously toxic to humans. These kernels need to be screened in the production process. Hyperspectral imaging techniques can be used to identify moldy peanuts. However, the changes in spectral information and texture information caused by the difference in moisture content in peanuts will affect the identification accuracy. To reduce and eliminate the influence of this factor, a data augmentation method based on interpolation was proposed to improve the generalization ability and robustness of the model. Firstly, the near-infrared hyperspectral images of 5 varieties, 4 classes, and 3 moisture content gradients with 39,119 kernels were collected. Then, the data augmentation method called the difference of spectral mean (DSM) was constructed. K-nearest neighbors (KNN), support vector machines (SVM), and MobileViT-xs models were used to verify the effectiveness of the data augmentation method on data with two gradients and three gradients. The experimental results show that the data augmentation can effectively reduce the influence of the difference in moisture content on the model identification accuracy. The DSM method has the highest accuracy improvement in 5 varieties of peanut datasets. In particular, the accuracy of KNN, SVM, and MobileViT-xs using the data of two gradients was improved by 3.55%, 4.42%, and 5.9%, respectively. Furthermore, this study provides a new method for improving the identification accuracy of moldy peanuts and also provides a reference basis for the screening of related foods such as corn, orange, and mango.
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Liu, Hong, Tao Yu, Bingliang Hu, Xingsong Hou, Zhoufeng Zhang, Xiao Liu, Jiacheng Liu, et al. "UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring." Remote Sensing 13, no. 20 (October 12, 2021): 4069. http://dx.doi.org/10.3390/rs13204069.
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Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV air–ground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 μg/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water.
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Dilmurat, K., V. Sagan, and S. Moose. "AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLE-BASED HYPERSPECTRAL AND LIDAR DATA FUSION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 193–99. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-193-2022.
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Abstract. The increased availability of remote sensing data combined with the wide-ranging applicability of artificial intelligence has enabled agriculture stakeholders to monitor changes in crops and their environment frequently and accurately. Applying cutting-edge technology in precision agriculture also enabled the prediction of pre-harvest yield from standing crop signals. Forecasting grain yield from standing crops benefits high-throughput plant phenotyping and agriculture policymaking with information on where crop production is likely to decline. Advanced developments in the Unmanned Aerial Vehicle (UAV) platform and sensor technologies aided high-resolution spatial, spectral, and structural data collection processes at a relatively lower cost and shorter time. In this study, UAV-based LiDAR and hyperspectral images were collected during the growing season of 2020 over a cornfield near Urbana Champaign, Illinois, USA. Hyperspectral imagery-based canopy spectral & texture features and LiDAR point cloud-based canopy structure features were extracted and, along with their combination, were used as inputs for maize yield prediction under the H2O Automated Machine Learning framework (H2O-AutoML). The research results are (1) UAV Hyperspectral imagery can successfully predict maize yield with relatively decent accuracies; additionally, LiDAR point cloud-based canopy structure features are found to be significant indicators for maize yield prediction, which produced slightly poorer, yet comparable results to hyperspectral data; (2) regardless of machine learning methods, integration of hyperspectral imagery-based canopy spectral and texture information with LiDAR-based canopy structure features outperformed the predictions when using a single sensor alone; (3)the H2O-AutoML framework presented to be an efficient strategy for machine learning-based data-driven model building.
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Eckstein, B. A., and R. Arlen. "IEEE PROJECT 4001 – STANDARDS FOR CHARACTERIZATION AND CALIBRATION OF HYPERSPECTRAL IMAGING DEVICES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 43–47. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-43-2021.
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Abstract. Hyperspectral imaging (HSI) systems have been invaluable tools for over two decades, but there are few authoritative standards that characterize these systems or define the data and metadata they produce. Manufacturers calibrate instruments and report specifications differently and, in some cases, the same term has different definitions among HSI programs.To address these inconsistencies, the Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) sponsored Project 4001 (P4001), a Hyperspectral Working Group under the auspices of IEEE’s Standards Association. Since its inception in 2018, the IEEE P4001 Working Group has been working to specify testing and characterization methods for HSI device manufacturers, as well as recommend data structures and terminology for HSI products.P4001 focuses on the ultraviolet through the shortwave infrared spectral range (~250 to 2500 nm) and prioritizes camera technologies that are in widespread use. Many aspects of the standard will have wider applicability with respect to camera technology and wavelength range, and updates will expand the range of technologies and topics covered. Industrial, laboratory and geoscience use cases are informing the development of the standard. Utilization of the P4001 HSI standard will lead to HSI systems with consistent characterization and calibration criteria, as well as interoperable data products with a common lexicon for data and metadata.
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Piñuela, F., D. Cerra, and R. Müller. "ENABLING SEARCHES ON WAVELENGTHS IN A HYPERSPECTRAL INDICES DATABASE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (October 19, 2017): 161–64. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-161-2017.
Повний текст джерелаАнотація:
Spectral indices derived from hyperspectral reflectance measurements are powerful tools to estimate physical parameters in a non-destructive and precise way for several fields of applications, among others vegetation health analysis, coastal and deep water constituents, geology, and atmosphere composition. In the last years, several micro-hyperspectral sensors have appeared, with both full-frame and push-broom acquisition technologies, while in the near future several hyperspectral spaceborne missions are planned to be launched. This is fostering the use of hyperspectral data in basic and applied research causing a large number of spectral indices to be defined and used in various applications. Ad hoc search engines are therefore needed to retrieve the most appropriate indices for a given application. In traditional systems, query input parameters are limited to alphanumeric strings, while characteristics such as spectral range/ bandwidth are not used in any existing search engine. Such information would be relevant, as it enables an inverse type of search: given the spectral capabilities of a given sensor or a specific spectral band, find all indices which can be derived from it. This paper describes a tool which enables a search as described above, by using the central wavelength or spectral range used by a given index as a search parameter. This offers the ability to manage numeric wavelength ranges in order to select indices which work at best in a given set of wavelengths or wavelength ranges.
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Torti, Emanuele, Raquel Leon, Marco La Salvia, Giordana Florimbi, Beatriz Martinez-Vega, Himar Fabelo, Samuel Ortega, Gustavo M. Callicó, and Francesco Leporati. "Parallel Classification Pipelines for Skin Cancer Detection Exploiting Hyperspectral Imaging on Hybrid Systems." Electronics 9, no. 9 (September 13, 2020): 1503. http://dx.doi.org/10.3390/electronics9091503.
Повний текст джерелаАнотація:
The early detection of skin cancer is of crucial importance to plan an effective therapy to treat the lesion. In routine medical practice, the diagnosis is based on the visual inspection of the lesion and it relies on the dermatologists’ expertise. After a first examination, the dermatologist may require a biopsy to confirm if the lesion is malignant or not. This methodology suffers from false positives and negatives issues, leading to unnecessary surgical procedures. Hyperspectral imaging is gaining relevance in this medical field since it is a non-invasive and non-ionizing technique, capable of providing higher accuracy than traditional imaging methods. Therefore, the development of an automatic classification system based on hyperspectral images could improve the medical practice to distinguish pigmented skin lesions from malignant, benign, and atypical lesions. Additionally, the system can assist general practitioners in first aid care to prevent noncritical lesions from reaching dermatologists, thereby alleviating the workload of medical specialists. In this paper is presented a parallel pipeline for skin cancer detection that exploits hyperspectral imaging. The computational times of the serial processing have been reduced by adopting multicore and many-core technologies, such as OpenMP and CUDA paradigms. Different parallel approaches have been combined, leading to the development of fifteen classification pipeline versions. Experimental results using in-vivo hyperspectral images show that a hybrid parallel approach is capable of classifying an image of 50 × 50 pixels with 125 bands in less than 1 s.
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Guyot, Alexandre, Marc Lennon, Nicolas Thomas, Simon Gueguen, Tristan Petit, Thierry Lorho, Serge Cassen, and Laurence Hubert-Moy. "Airborne Hyperspectral Imaging for Submerged Archaeological Mapping in Shallow Water Environments." Remote Sensing 11, no. 19 (September 25, 2019): 2237. http://dx.doi.org/10.3390/rs11192237.
Повний текст джерелаАнотація:
Nearshore areas around the world contain a wide variety of archeological structures, including prehistoric remains submerged by sea level rise during the Holocene glacial retreat. While natural processes, such as erosion, rising sea level, and exceptional climatic events have always threatened the integrity of this submerged cultural heritage, the importance of protecting them is becoming increasingly critical with the expanding effects of global climate change and human activities. Aerial archaeology, as a non-invasive technique, contributes greatly to documentation of archaeological remains. In an underwater context, the difficulty of crossing the water column to reach the bottom and its potential archaeological information usually requires active remote-sensing technologies such as airborne LiDAR bathymetry or ship-borne acoustic soundings. More recently, airborne hyperspectral passive sensors have shown potential for accessing water-bottom information in shallow water environments. While hyperspectral imagery has been assessed in terrestrial continental archaeological contexts, this study brings new perspectives for documenting submerged archaeological structures using airborne hyperspectral remote sensing. Airborne hyperspectral data were recorded in the Visible Near Infra-Red (VNIR) spectral range (400–1000 nm) over the submerged megalithic site of Er Lannic (Morbihan, France). The method used to process these data included (i) visualization of submerged anomalous features using a minimum noise fraction transform, (ii) automatic detection of these features using Isolation Forest and the Reed–Xiaoli detector and (iii) morphological and spectral analysis of archaeological structures from water-depth and water-bottom reflectance derived from the inversion of a radiative transfer model of the water column. The results, compared to archaeological reference data collected from in-situ archaeological surveys, showed for the first time the potential of airborne hyperspectral imagery for archaeological mapping in complex shallow water environments.
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Wei, Yali, Mengxi Wang, Yuwen Ma, Zhenni Que, and Dengbo Yao. "Classical Dichotomy of Macrophages and Alternative Activation Models Proposed with Technological Progress." BioMed Research International 2021 (October 21, 2021): 1–10. http://dx.doi.org/10.1155/2021/9910596.
Повний текст джерелаАнотація:
Macrophages are important immune cells that participate in the regulation of inflammation in implant dentistry, and their activation/polarization state is considered to be the basis for their functions. The classic dichotomy activation model is commonly accepted, however, due to the discovery of macrophage heterogeneity and more functional and iconic exploration at different technologies; some studies have discovered the shortcomings of the dichotomy model and have put forward the concept of alternative activation models through the application of advanced technologies such as cytometry by time-of-flight (CyTOF), single-cell RNA-seq (scRNA-seq), and hyperspectral image (HSI). These alternative models have great potential to help macrophages divide phenotypes and functional genes.
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Bayarri, Vicente, Elena Castillo, Sergio Ripoll, and Miguel A. Sebastián. "Improved Application of Hyperspectral Analysis to Rock Art Panels from El Castillo Cave (Spain)." Applied Sciences 11, no. 3 (February 1, 2021): 1292. http://dx.doi.org/10.3390/app11031292.
Повний текст джерелаАнотація:
Rock art is one of the most fragile and relevant cultural phenomena in world history, carried out in shelters or the walls and ceilings of caves with mineral and organic substances. The fact it has been preserved until now can be considered as fortunate since both anthropogenic and natural factors can cause its disappearance or deterioration. This is the reason why rock art needs special conservation and protection measures. The emergence of digital technologies has made a wide range of tools and programs available to the community for a more comprehensive documentation of rock art in both 2D and 3D. This paper shows a workflow that makes use of visible and near-infrared hyperspectral technology to manage, monitor and preserve this appreciated cultural heritage. Hyperspectral imaging is proven to be an efficient tool for the recognition of figures, coloring matter, and state of conservation of such valuable art.
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Liu, Huajian, Brooke Bruning, Trevor Garnett, and Bettina Berger. "Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing." Computers and Electronics in Agriculture 175 (August 2020): 105621. http://dx.doi.org/10.1016/j.compag.2020.105621.
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