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Journal articles on the topic 'Signature hyperspectrale'

Author: Grafiati

Published: 25 May 2024

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1

Papp, Adam, Julian Pegoraro, Daniel Bauer, Philip Taupe, Christoph Wiesmeyr, and Andreas Kriechbaum-Zabini. "Automatic Annotation of Hyperspectral Images and Spectral Signal Classification of People and Vehicles in Areas of Dense Vegetation with Deep Learning." Remote Sensing 12, no. 13 (2020): 2111. http://dx.doi.org/10.3390/rs12132111.

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Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train

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Gromov, V. P., L. I. Lebedev, and V. E. Turlapov. "Analysis and object markup of hyperspectral images for machine learning methods." Information Technology and Nanotechnology, no. 2391 (2019): 309–17. http://dx.doi.org/10.18287/1613-0073-2019-2391-309-317.

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The development of the nominal sequence of steps for analyzing the HSI proposed by Landgrebe, which is necessary in the context of the appearance of reference signature libraries for environmental monitoring, is discussed. The approach is based on considering the HSI pixel as a signature that stores all spectral features of an object and its states, and the HSI as a whole - as a two-dimensional signature field. As a first step of the analysis, a procedure is proposed for detecting a linear dependence of signatures by the magnitude of the Pearson correlation coefficient. The main apparatus of a

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Hartfield, Kyle, Jeffrey K. Gillan, Cynthia L. Norton, Charles Conley, and Willem J. D. van Leeuwen. "A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions." Land 11, no. 6 (2022): 786. http://dx.doi.org/10.3390/land11060786.

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Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti’s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the a

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MESSINGER, DAVID W., CARL SALVAGGIO, and NATALIE M. SINISGALLI. "DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES." International Journal of High Speed Electronics and Systems 17, no. 04 (2007): 801–12. http://dx.doi.org/10.1142/s0129156407004990.

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Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A

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Lebedev, L. I., Yu V. Yasakov, T. H. Utesheva, V. P. Gromov, A. V. Borusjak, and V. E. Turlapov. "Complex analysis and monitoring of the environment based on earth sensing data." Computer Optics 43, no. 2 (2019): 282–95. http://dx.doi.org/10.18287/2412-6179-2019-43-2-282-295.

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We study a problem of complex analysis and monitoring of the environment based on Earth Sensing Data, with the emphasis on the use of hyperspectral images (HSI), and propose a solution based on developing algorithmic procedures for HSI processing and storage. HSI is considered as a two-dimensional field of pixel signatures. Methods are proposed for evaluating the similarity of a HSI pixel signature with a reference signature, via simple alignment transformations: identical; amplitude scaling; shift along y-axis; and a combination of the last two. A clustering / recognition method with self-lea

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Jamaludin, Muhammad Ikhwan, Abdul Nasir Matori, Mohammad Faize Kholik, and Munirah Mohd Mokhtar. "Development Spectral Library of Vegetation Stress for Hydrocarbon Seepage." Applied Mechanics and Materials 567 (June 2014): 693–98. http://dx.doi.org/10.4028/www.scientific.net/amm.567.693.

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Spectral Library is data archive of spectral signatures of various natural and man-made materials. In oil and gas industry, spectral library might not be heard often, but these tools can pose a great help for future oil and gas exploration. A developing spectral library for hydrocarbon is basically a new advancement in this field, and this project may implement the spectral library on global hydrocarbon seeps in the future. In this paper, the procedure in the developing spectral library from vegetation stress was demonstrated. In order to obtain these spectral signatures of hydrocarbon, the us

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Licciardi, Giorgio, Costantino Del Gaudio, and Jocelyn Chanussot. "Non-Linear Spectral Unmixing for the Estimation of the Distribution of Graphene Oxide Deposition on 3D Printed Composites." Applied Sciences 10, no. 21 (2020): 7792. http://dx.doi.org/10.3390/app10217792.

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Hyperspectral analysis is a well-established technique that can be suitably implemented in several application fields, including materials science. This approach allows us to deal with data samples containing spatial and spectral information at very high resolution, thus enabling us to evaluate materials properties at a nanoscale level. As a proof of concept, hyperspectral imaging was here considered to investigate 3D printed polymer matrix composites, considering graphene oxide (GO) as a nanofiller. Commercial polycaprolactone and polylactic acid filaments were firstly treated with GO to be t

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Ma, Pengfei, Jiaoli Li, Ying Zhuo, Pu Jiao, and Genda Chen. "Coating Condition Detection and Assessment on the Steel Girder of a Bridge through Hyperspectral Imaging." Coatings 13, no. 6 (2023): 1008. http://dx.doi.org/10.3390/coatings13061008.

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The organic coating of bridge steel girders is subjected to physical scratches, corrosion, and aging in natural weathering. The breakdown of the coating may cause serviceability and safety problems if left unnoticed. Conventional coating inspection is time-consuming and lacks information about the coating’s chemical integrity. A hyperspectral imaging method is proposed to detect the condition of steel coatings based on coating-responsive features in reflectance spectra. A field test was conducted on the real-world bridge, which shows obvious signs of degradation. The hyperspectral signature en

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Srinivas, Umamahesh, Yi Chen, Vishal Monga, Nasser Nasrabadi, and Trac Tran. "Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models." Geoscience and Remote Sensing Letters, IEEE 10, no. 3 (2012): 505–9. http://dx.doi.org/10.1109/lgrs.2012.2211858.

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A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the cl

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SCHAUM, A. "ADVANCED HYPERSPECTRAL ALGORITHMS FOR TACTICAL TARGET DETECTION AND DISCRIMINATION." International Journal of High Speed Electronics and Systems 18, no. 03 (2008): 531–44. http://dx.doi.org/10.1142/s0129156408005540.

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Region-of-interest cueing by hyperspectral imaging systems for tactical reconnaissance has emphasized wide area coverage, low false alarm rates, and the search for manmade objects. Because they often appear embedded in complex environments and can exhibit large intrinsic spectral variability, these targets usually cannot be characterized by consistent signatures that might facilitate the detection process. Template matching techniques that focus on distinctive and persistent absorption features, such as those characterizing gases or liquids, prove ineffectual for most hard-body targets. High-p

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Wang, Jing. "Progressive coding for hyperspectral signature characterization." Optical Engineering 45, no. 9 (2006): 097002. http://dx.doi.org/10.1117/1.2353113.

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Yueming Wang, Yueming Wang, Feng Xie Feng Xie, and and Jianyu Wang and Jianyu Wang. "Short-wave infrared signature and detection of aicraft in flight based on space-borne hyperspectral imagery." Chinese Optics Letters 14, no. 12 (2016): 122801–4. http://dx.doi.org/10.3788/col201614.122801.

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Bacca, Jorge Luis, and Henry Arguello. "Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels." TecnoLógicas 22, no. 46 (2019): 1–14. http://dx.doi.org/10.22430/22565337.1205.

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Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces. Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised class

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Kim, Sungho, Jungho Kim, Jinyong Lee, and Junmo Ahn. "AS-CRI: A New Metric of FTIR-Based Apparent Spectral-Contrast Radiant Intensity for Remote Thermal Signature Analysis." Remote Sensing 11, no. 7 (2019): 777. http://dx.doi.org/10.3390/rs11070777.

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Infrared signature analysis that considers both the target and background is fundamentally important to the development of target detection systems as well as in the design of ships for thermal stealth. This paper presents the analysis results of long-term infrared signature variations in terms of the apparent spectral-contrast radiant intensity measured using Fourier transform infrared (FTIR)-based hyperspectral images. A novel apparent spectral-contrast radiant intensity (AS-CRI) measure is proposed to evaluate the spectral infrared signature accurately at the sensor point of view. The spect

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Kasilingam, Cendrayan, Arisiyappan Thirunavukkarasu, and Chandran Ramachandran. "Spectral signatures for iron ore deposits in Tirthamalai area, Dharmapuri District, Tamil Nadu, India." Journal of Applied and Natural Science 15, no. 1 (2023): 107–15. http://dx.doi.org/10.31018/jans.v15i1.4160.

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The demand for iron ore has increased recently and employing geochemical and hyperspectral remote sensing techniques for discovering new ore and mineral resources have primarily been concentrated on the economic phases. The present study aimed to characterize the hyperspectral spectral signatures of iron ores of field samples to map the deposits that occurred in the Tirthamalai hill region, which lies in the parts of Harur Taluk, Dharmapuri district of Tamil Nadu state, India The measurement and study of spectral signatures of the different samples of the deposits showed strong spectral absorp

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Raychaudhuri, Barun. "Synthesis of mixed pixel hyperspectral signatures." International Journal of Remote Sensing 33, no. 6 (2011): 1954–66. http://dx.doi.org/10.1080/01431161.2011.610378.

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Honkavaara, E., T. Hakala, O. Nevalainen, et al. "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.

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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. F

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Honkavaara, E., T. Hakala, O. Nevalainen, et al. "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.

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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. F

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Khandelwal, A., and K. S. Rajan. "Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 179–85. http://dx.doi.org/10.5194/isprsannals-ii-8-179-2014.

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In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applicat

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Miljković, V., and D. Gajski. "ADAPTATION OF INDUSTRIAL HYPERSPECTRAL LINE SCANNER FOR ARCHAEOLOGICAL APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 343–45. http://dx.doi.org/10.5194/isprs-archives-xli-b5-343-2016.

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The spectral characteristic of the visible light reflected from any of archaeological artefact is the result of the interaction of its surface illuminated by incident light. Every particular surface depends on what material it is made of and/or which layers put on it has its spectral signature. Recent archaeometry recognises this information as very valuable data to extend present documentation of artefacts and as a new source for scientific exploration. However, the problem is having an appropriate hyperspectral imaging system available and adopted for applications in archaeology. In this pap

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Miljković, V., and D. Gajski. "ADAPTATION OF INDUSTRIAL HYPERSPECTRAL LINE SCANNER FOR ARCHAEOLOGICAL APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 343–45. http://dx.doi.org/10.5194/isprsarchives-xli-b5-343-2016.

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The spectral characteristic of the visible light reflected from any of archaeological artefact is the result of the interaction of its surface illuminated by incident light. Every particular surface depends on what material it is made of and/or which layers put on it has its spectral signature. Recent archaeometry recognises this information as very valuable data to extend present documentation of artefacts and as a new source for scientific exploration. However, the problem is having an appropriate hyperspectral imaging system available and adopted for applications in archaeology. In this pap

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Urbina Ortega, Carlos, Eduardo Quevedo Gutiérrez, Laura Quintana, et al. "Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples." Sensors 23, no. 4 (2023): 1863. http://dx.doi.org/10.3390/s23041863.

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Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image p

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Chang, Chein-I., Sumit Chakravarty, Hsian-Min Chen, and Yen-Chieh Ouyang. "Spectral derivative feature coding for hyperspectral signature analysis." Pattern Recognition 42, no. 3 (2009): 395–408. http://dx.doi.org/10.1016/j.patcog.2008.07.016.

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Pervez, W., S. A. Khan, and Valiuddin. "HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 169–75. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-169-2015.

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Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is

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Babić, L., V. Miljković, I. Odak, and A. Đapo. "HYPERSPECTRAL IMAGING IN PRESERVATION OF CROATIA’S HISTORIC TREES: A CASE STUDY OF DEDEK OAK IN MAKSIMIR PARK." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1853–59. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1853-2023.

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Abstract. This study focuses on the application of hyperspectral imaging techniques for the genetic preservation of historic trees, specifically investigating the 600-year-old "Dedek" Oak in Maksimir Park, Croatia. The research aims to contribute to the conservation of Croatia's natural heritage and underscore the potential of hyperspectral imaging in tree preservation. The Croatian Forest Research Institute has been actively involved in preserving the pedunculate oak trees, recognizing their cultural and ecological significance. Efforts have been made to cultivate seedlings from the original

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Lazzeri, Giacomo, William Frodella, Guglielmo Rossi, and Sandro Moretti. "Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy." Sensors 21, no. 12 (2021): 3982. http://dx.doi.org/10.3390/s21123982.

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Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote s

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El-Sharkawy, Yasser H., Sherif Elbasuney, Sara M. Radwan, Mostafa A. Askar, and Gharieb S. El-Sayyad. "Total RNA nonlinear polarization: towards facile early diagnosis of breast cancer." RSC Advances 11, no. 53 (2021): 33319–25. http://dx.doi.org/10.1039/d1ra05599b.

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Nonlinear polarization has been considered as a marvelous tool for several medical applications, and the capability to monitor any changes in RNA's spectral signature due to breast cancer was evaluated by hyperspectral camera.

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Carrasco-García, María Gema, María Inmaculada Rodríguez-García, Juan Jesús Ruíz-Aguilar, Lipika Deka, David Elizondo, and Ignacio José Turias Domínguez. "Oil Spill Classification Using an Autoencoder and Hyperspectral Technology." Journal of Marine Science and Engineering 12, no. 3 (2024): 495. http://dx.doi.org/10.3390/jmse12030495.

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Hyperspectral technology has been playing a leading role in monitoring oil spills in marine environments, which is an issue of international concern. In the case of monitoring oil spills in local areas, hyperspectral technology of small dimensions is the ideal solution. This research explores the use of encoded hyperspectral signatures to develop automated classifiers capable of discriminating between polluted and clean water and distinguishing between various types of oil. The overall objective is to leverage these classifiers to be able to improve the performance of conventional systems that

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Chang, Chein-I., Sumit Chakravarty, Chien-Shun Lo, and Chinsu Lin. "Spectral Feature Probabilistic Coding for Hyperspectral Signatures." IEEE Sensors Journal 10, no. 3 (2010): 395–409. http://dx.doi.org/10.1109/jsen.2009.2038119.

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Vyas, Saurabh, Amit Banerjee, and Philippe Burlina. "Estimating physiological skin parameters from hyperspectral signatures." Journal of Biomedical Optics 18, no. 5 (2013): 057008. http://dx.doi.org/10.1117/1.jbo.18.5.057008.

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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 (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 calibrat

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Khoshsokhan, S., R. Rajabi, and H. Zayyani. "DISTRIBUTED UNMIXING OF HYPERSPECTRAL DATAWITH SPARSITY CONSTRAINT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 145–50. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-145-2017.

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Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm,

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de Castro, Ana-Isabel, Montserrat Jurado-Expósito, María-Teresa Gómez-Casero, and Francisca López-Granados. "Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops." Scientific World Journal 2012 (2012): 1–11. http://dx.doi.org/10.1100/2012/630390.

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In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC)

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Dhole, Pravin V., Vijay D. Dhangar, Sulochana D. Shejul, and Prof Bharti W. Gawali. "Machine Learning Approach for Spectral Signature Based Chemical Composition Analysis using Hyperspectral Data." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 279–86. http://dx.doi.org/10.22214/ijraset.2023.55922.

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Abstract: Number of techniques to identify falsified medications, including thin layer chromatography (TLC), analytical methods, eye examination, and the use of sophisticated labs with strong equipment and technical specialists. Such techniques take more time and call for sophisticated labs, specialized specialists in that field, and sample preparation. This research uses hyperspectral data to develop a spectral pattern for identifying falsified medications. Near infrared spectroscopic techniques are commonly used for this task because of their many advantages. For this research, we used the w

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Kuska, Matheus Thomas, Anna Brugger, Stefan Thomas, et al. "Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei." Phytopathology® 107, no. 11 (2017): 1388–98. http://dx.doi.org/10.1094/phyto-04-17-0128-r.

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Differences in early plant–pathogen interactions are mainly characterized by using destructive methods. Optical sensors are advanced techniques for phenotyping host–pathogen interactions on different scales and for detecting subtle plant resistance responses against pathogens. A microscope with a hyperspectral camera was used to study interactions between Blumeria graminis f. sp. hordei and barley (Hordeum vulgare) genotypes with high susceptibility or resistance due to hypersensitive response (HR) and papilla formation. Qualitative and quantitative assessment of pathogen development was used

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Sadeghi-Tehran, Pouria, Nicolas Virlet, and Malcolm J. Hawkesford. "A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery." Remote Sensing 13, no. 5 (2021): 898. http://dx.doi.org/10.3390/rs13050898.

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(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus,

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Zhelezova, Sofia V., Elena V. Pakholkova, Vladislav E. Veller, et al. "Hyperspectral Non-Imaging Measurements and Perceptron Neural Network for Pre-Harvesting Assessment of Damage Degree Caused by Septoria/Stagonospora Blotch Diseases of Wheat." Agronomy 13, no. 4 (2023): 1045. http://dx.doi.org/10.3390/agronomy13041045.

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The detection and identification of plant diseases is a fundamental task for sustainable crop production. Septoria tritici and Stagonospora nodorum blotch (STB and SNB) are two of the most common diseases of cereal crops that cause significant economic damage. Both pathogens are difficult to identify at early stages of infection. Determining the degree of the disease at a late infection stage is useful for assessing cereal crops before harvesting, as it allows the assessment of potential yield losses. Hyperspectral sensing could allow for automatic recognition of Septoria harmfulness on wheat

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Chakravortty, S., and P. Subramaniam. "Fusion of Hyperspectral and Multispectral Image Data for Enhancement of Spectral and Spatial Resolution." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1099–103. http://dx.doi.org/10.5194/isprsarchives-xl-8-1099-2014.

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Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has

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Che’Ya, Nik Norasma, Nur Adibah Mohidem, Nor Athirah Roslin, et al. "Mobile Computing for Pest and Disease Management Using Spectral Signature Analysis: A Review." Agronomy 12, no. 4 (2022): 967. http://dx.doi.org/10.3390/agronomy12040967.

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The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes; one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and precautions to prevent further infection from occurring. Furthermore, farmers can communicate with agricultural authorities to manage their farm from home, and efficiently obtain information such as the spectral signature of crops. Therefore, the spectral signature can be used as a reference to detect pests an

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Chein-I Chang. "Target signature-constrained mixed pixel classification for hyperspectral imagery." IEEE Transactions on Geoscience and Remote Sensing 40, no. 5 (2002): 1065–81. http://dx.doi.org/10.1109/tgrs.2002.1010894.

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Wang, Su, Chuin-Mu Wang, Mann-Li Chang, Ching-Tsorng Tsai, and Chein-I. Chang. "Applications of Kalman Filtering to Single Hyperspectral Signature Analysis." IEEE Sensors Journal 10, no. 3 (2010): 547–63. http://dx.doi.org/10.1109/jsen.2009.2038546.

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Yang, Zhonglin, Yanhua Cao, Shutian Liu, et al. "A Novel Signature and Authentication Cryptosystem for Hyperspectral Image by Using Triangular Association Encryption Algorithm in Gyrator Domains." Applied Sciences 12, no. 15 (2022): 7649. http://dx.doi.org/10.3390/app12157649.

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A novel optical signature and authentication cryptosystem is proposed by applying triangular association encryption algorithm (TAEA) and 3D Arnold transform in Gyrator domains. Firstly, a triangular association encryption algorithm (TAEA) is designed, which makes it possible to turn the diffusion of pixel values within bands into the diffusion within and between bands. Besides, the image signature function is considered and utilized in the proposed cryptosystem. Without the image signature, the original image cannot be restored even if all of the keys are obtained. Moreover, the image integrit

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Olyaei, Mohammadali, and Ardeshir Ebtehaj. "Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms." Remote Sensing 16, no. 1 (2023): 172. http://dx.doi.org/10.3390/rs16010172.

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This article provides insights into the optical signatures of plastic litter based on a published laboratory-scale reflectance data set (350–2500 nm) of dry and wet plastic debris under clear and turbid waters using different band selection techniques, including sparse variable selection, density peak clustering, and hierarchical clustering. The variable selection method identifies important wavelengths by minimizing a reconstruction error metric, while clustering approaches rely on the strengths of the correlation and local density of the spectra. Analyses of the data reveal three distinct ab

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Banerjee, Bikram Pratap, and Simit Raval. "A Particle Swarm Optimization Based Approach to Pre-tune Programmable Hyperspectral Sensors." Remote Sensing 13, no. 16 (2021): 3295. http://dx.doi.org/10.3390/rs13163295.

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Identification of optimal spectral bands often involves collecting in-field spectral signatures followed by thorough analysis. Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), requiring a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. An innovative workflow has been designed and implemented to simplify the process of in-field spectral sampling an

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Sture, Øystein, Ben Snook, and Martin Ludvigsen. "Obtaining Hyperspectral Signatures for Seafloor Massive Sulphide Exploration." Minerals 9, no. 11 (2019): 694. http://dx.doi.org/10.3390/min9110694.

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Seafloor massive sulphide (SMS) deposits are hosts to a wide range of economic minerals, and may become an important resource in the future. The exploitation of these resources is associated with considerable expenses, and a return on investment may depend on the availability of multiple deposits. Therefore, efficient exploration methodologies for base metal deposits are important for future deep sea mining endeavours. Underwater hyperspectral imaging (UHI) has been demonstrated to be able to differentiate between different types of materials on the seafloor. The identification of possible end

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Alizade Naeini, A., A. Jamshidzadeh, M. Saadatseresht, and S. Homayouni. "AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 35–39. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-35-2014.

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K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman–Pearson detection theory based eigen-

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Ma, Chen, Junjun Jiang, Huayi Li, Xiaoguang Mei, and Chengchao Bai. "Hyperspectral Image Classification via Spectral Pooling and Hybrid Transformer." Remote Sensing 14, no. 19 (2022): 4732. http://dx.doi.org/10.3390/rs14194732.

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Hyperspectral images (HSIs) contain spatially structured information and pixel-level sequential spectral attributes. The continuous spectral features contain hundreds of wavelength bands and the differences between spectra are essential for achieving fine-grained classification. Due to the limited receptive field of backbone networks, convolutional neural networks (CNNs)-based HSI classification methods show limitations in modeling spectral-wise long-range dependencies with fixed kernel size and a limited number of layers. Recently, the self-attention mechanism of transformer framework is intr

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Shao, Zhenfeng, Weixun Zhou, Qimin Cheng, Chunyuan Diao, and Lei Zhang. "An effective hyperspectral image retrieval method using integrated spectral and textural features." Sensor Review 35, no. 3 (2015): 274–81. http://dx.doi.org/10.1108/sr-10-2014-0716.

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Purpose – The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene. Design/methodology/approach – In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal compone

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JIJÓN-PALMA, Mario Ernesto, Caisse AMISSE, Jaime Carlos MACUÁCUA, and Jorge Antonio Silva CENTENO. "Noisy band selection based on the integration of the Stacked-Autoencoder and Convolutional Neural Network approaches for hyperspectral data." Geosciences = Geociências 42, no. 2 (2023): 269–80. http://dx.doi.org/10.5016/geociencias.v42i2.16976.

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The presence of noise on hyperspectral images causes degradation and hinders efficiency of processing for land cover classification. In this sense, removing noise or detecting noisy bands automatically on hyperspectral images becomes a challenge for research in remote sensing. To cope this problem, an integrated model (SAE-1DCNN) is presented in this study, based on Stacked-Autoencoders (SAE) and Convolutional Neural Networks (CNN) algorithms for the selection and exclusion of noisy bands. The proposed model employs convolutional layers to improve the performance of autoencoders focused on dis

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Agrawal, Rajesh, and Narendra Bawane. "Adaptive Lifting Transform for Classification of Hyperspectral Signatures." Advances in Remote Sensing 04, no. 02 (2015): 138–46. http://dx.doi.org/10.4236/ars.2015.42012.

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