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Gotowa bibliografia na temat „SPECTRAL - SPATIAL STRATEGIES”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "SPECTRAL - SPATIAL STRATEGIES"

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Wefers, Stefanie, Ashish Karmacharya, and Frank Boochs. "Development of a platform recommending 3D and spectral digitisation strategies." Virtual Archaeology Review 7, no. 15 (2016): 18. http://dx.doi.org/10.4995/var.2016.5861.

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<p class="VARAbstract" align="left">Spatial and spectral recording of cultural heritage objects is a complex task including data acquisition, processing and analysis involving different technical disciplines. Additionally, the development of a suitable digitisation strategy satisfying the expectations of the humanities experts needs an interdisciplinary dialogue often suffering from misunderstanding and knowledge gaps on both the technical and humanities sides.</p><p class="VARAbstract" align="left">Through a concerted discussion experts from the cultural heritage and technic

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Gircys, Michael, and Brian J. Ross. "Image Evolution Using 2D Power Spectra." Complexity 2019 (January 2, 2019): 1–21. http://dx.doi.org/10.1155/2019/7293193.

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Procedurally generated images and textures have been widely explored in evolutionary art. One active research direction in the field is the discovery of suitable heuristics for measuring perceived characteristics of evolved images. This is important in order to help influence the nature of evolved images and thereby evolve more meaningful and pleasing art. In this regard, particular challenges exist for quantifying aspects of style and shape. In an attempt to bridge the divide between computer vision and cognitive perception, we propose the use of measures related to image spatial frequencies.

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Maurer, Hansruedi, Stewart Greenhalgh, and Sabine Latzel. "Frequency and spatial sampling strategies for crosshole seismic waveform spectral inversion experiments." GEOPHYSICS 74, no. 6 (2009): WCC79—WCC89. http://dx.doi.org/10.1190/1.3157252.

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Analyses of synthetic frequency-domain acoustic waveform data provide new insights into the design and imaging capability of crosshole surveys. The full complex Fourier spectral data offer significantly more information than other data representations such as the amplitude, phase, or Hartley spectrum. Extensive eigenvalue analyses are used for further inspection of the information content offered by the seismic data. The goodness of different experimental configurations is investigated by varying the choice of (1) the frequencies, (2) the source and receiver spacings along the boreholes, and (

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Huang, Leping, Zhongwen Hu, Xin Luo, Qian Zhang, Jingzhe Wang, and Guofeng Wu. "Stepwise Fusion of Hyperspectral, Multispectral and Panchromatic Images with Spectral Grouping Strategy: A Comparative Study Using GF5 and GF1 Images." Remote Sensing 14, no. 4 (2022): 1021. http://dx.doi.org/10.3390/rs14041021.

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Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decompos

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Zheng, Cao, Lv, and Benediktsson. "Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images." Remote Sensing 11, no. 16 (2019): 1903. http://dx.doi.org/10.3390/rs11161903.

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In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complemen

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Liang, Fan, Cheng Qian, Wei Yu, David Griffith, and Nada Golmie. "Survey of Graph Neural Networks and Applications." Wireless Communications and Mobile Computing 2022 (July 28, 2022): 1–18. http://dx.doi.org/10.1155/2022/9261537.

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The advance of deep learning has shown great potential in applications (speech, image, and video classification). In these applications, deep learning models are trained by datasets in Euclidean space with fixed dimensions and sequences. Nonetheless, the rapidly increasing demands on analyzing datasets in non-Euclidean space require additional research. Generally speaking, finding the relationships of elements in datasets and representing such relationships as weighted graphs consisting of vertices and edges is a viable way of analyzing datasets in non-Euclidean space. However, analyzing the w

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Chandler, Chris J., Silvia Valery Ávila-Mosqueda, Evelyn Raquel Salas-Acosta, et al. "Spectral Characteristics of Beached Sargassum in Response to Drying and Decay over Time." Remote Sensing 15, no. 17 (2023): 4336. http://dx.doi.org/10.3390/rs15174336.

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The bloom of pelagic Sargassum in the Atlantic Ocean has become increasingly problematic, especially when the algae have beached. A build-up of decaying beached material has damaging effects on coastal ecosystems and tourism industries. While remote sensing offers an effective tool to assess the spatial and temporal patterns of Sargassum over large spatial extents, its use so far has been limited to a broad discrimination of Sargassum species from other macroalgae and floating vegetation. Knowledge on the spatial distribution of decayed material will help to support management strategies and i

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Gojani, Ardian B., Dávid J. Palásti, Andrea Paul, Gábor Galbács, and Igor B. Gornushkin. "Analysis and Classification of Liquid Samples Using Spatial Heterodyne Raman Spectroscopy." Applied Spectroscopy 73, no. 12 (2019): 1409–19. http://dx.doi.org/10.1177/0003702819863847.

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Spatial heterodyne spectroscopy (SHS) is used for quantitative analysis and classification of liquid samples. SHS is a version of a Michelson interferometer with no moving parts and with diffraction gratings in place of mirrors. The instrument converts frequency-resolved information into a spatially resolved one and records it in the form of interferograms. The back-extraction of spectral information is done by the fast Fourier transform. A SHS instrument is constructed with the resolving power 5000 and spectral range 522–593 nm. Two original technical solutions are used as compared to previou

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Sun, Jun, Junbo Zhang, Xuesong Gao, et al. "Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder–Decoder Networks." Remote Sensing 14, no. 9 (2022): 1968. http://dx.doi.org/10.3390/rs14091968.

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In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges. Existing methods cannot extract spatial and spectral-channel contextual information in a targeted manner. In this paper, we propose an encoder–decoder network that fuses spatial attention and spectral-channel attention for HSI classification from three public HSI datasets to tackle these issues. In terms of feature information fusion, a multi-source attention mechanism including spatial and sp

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Miao, Zelang, and Wenzhong Shi. "A New Methodology for Spectral-Spatial Classification of Hyperspectral Images." Journal of Sensors 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1538973.

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Recent developments in hyperspectral images have heightened the need for advanced classification methods. To reach this goal, this paper proposed an improved spectral-spatial method for hyperspectral image classification. The proposed method mainly consists of three steps. First, four band selection strategies are proposed to utilize the statistical region merging (SRM) method to segment the hyperspectral image. The segmentation map is subsequently integrated with the pixel-wise classification method to classify the hyperspectral image. Finally, the final classification result is obtained usin

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