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Dual-Band Bandpass Frequency-Selective Structures With Arbitrary Band Ratios
IEEE Transactions on Antennas and Propagation, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Bo, Shen, Zhongxiang
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CLODD based band group selection
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of ...
Muhammad A. Islam +3 more
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Hyperspectral Band Selection: A Review
IEEE Geoscience and Remote Sensing Magazine, 2019A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational costs while preserving the significant spectral ...
Weiwei Sun, Qian Du
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Band selection procedure for multispectral scanners
Applied Optics, 1994Advances in high spectral resolution sensors and in data handling capabilities are enabling development of greatly improved remote-sensing devices for resource monitoring, so that design trade-offs are required. A methodology for optimizing selection of spectral bands for multispectral instruments such as those on the LANDSAT series of satellites is ...
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Background adaptive multispectral band selection
SPIE Proceedings, 2001AN initial automated band selection algorithm suitable for real-time application with tunable multispectral cameras is presented for multispectral target detection. The method and algorithm were developed from analyses of several background and target signatures collected from a field test using the prototype Tunable Filter Multispectral Camera (TFMC).
Frank J. Crosby +3 more
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Methodology for Hyperspectral Band Selection
Photogrammetric Engineering & Remote Sensing, 2004While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy.
Peter Bajcsy, Peter Groves
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Band selection based hyperspectral unmixing
2009 IEEE International Workshop on Imaging Systems and Techniques, 2009Hyperspectral unmixing is the procedure by which the measured spectrum of a mixed pixel is decomposed into a collection of constituent spectra, or endmembers, and their mixing proportions. However, due to the hundreds of spectral bands contained in the hyperspectral imagery, the large amount of data not only increase the computational loads, but also ...
Sen Jia, Zhen Ji, Yuntao Qian
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Sensitivities of Band-Edge Selectivities
Analog Integrated Circuits and Signal Processing, 1999Many filters have a band-edge selectivity (BES) that is a function of parameters other than filter order. In these cases, the band-edge selectivity can be maximized without increasing the order of the filter. However, in increasing BES, we must keep track of the sensitivity of BES to the parameters.
Celestino A. Corral +2 more
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Band Selection Through Redundancy Elimination
2013In this chapter, we discuss a method for selection of a few specific bands to accomplish a faster fusion of hyperspectral images, without much sacrificing the quality of the result of fusion. We present an information theoretic strategy for choosing specific image bands of the hyperspectral data cube using only the input hyperspectral image.
Subhasis Chaudhuri, Ketan Kotwal
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Band Instrument Selection and Assignment
Update: Applications of Research in Music Education, 2016This review of the literature examines the process of matching students with band instruments as presented in academic research journals and practitioner publications. While some directors may evaluate the potential impact of students’ physical characteristics such as lip size and shape, teeth and jaw structure, body build, and so forth, other sources
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