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Thematische Bibliographien / Signature hyperspectrale

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Auswahl der wissenschaftlichen Literatur zum Thema „Signature hyperspectrale“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Signature hyperspectrale"

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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