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Friday, July 31, 2020 | History

4 edition of Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery X found in the catalog.

Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery X

12-15 April, 2004, Orlando, Florida, USA

  • 121 Want to read
  • 8 Currently reading

Published by SPIE in Bellingham, Wash .
Written in English

    Subjects:
  • Remote sensing -- Congresses.,
  • Image processing -- Digital techniques -- Congresses.

  • Edition Notes

    Other titlesAlgorithms for multispectral, hyperspectral, and ultraspectral imagery.
    StatementSylvia S. Shen, Paul E. Lewis, chairs/editors ; sponsored and published by SPIE--the International Society for Optical Engineering.
    GenreCongresses.
    SeriesSPIE proceedings series,, 5425, Proceedings of SPIE--the International Society for Optical Engineering ;, v. 5425.
    ContributionsShen, Sylvia S., Lewis, Paul E., Society of Photo-optical Instrumentation Engineers.
    Classifications
    LC ClassificationsG70.4 .A525 2004
    The Physical Object
    Paginationxiii, 674 p. :
    Number of Pages674
    ID Numbers
    Open LibraryOL3435373M
    ISBN 10081945348X
    LC Control Number2005278216
    OCLC/WorldCa56359229

    Erdinc Gunes, S.E. Yuksel, "Effect of endmember clustering on proportion estimation: results on the Share dataset", SPIE Defense and Security: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, April pdf Link: Di Bai, David W. Messinger, David Howell, "A pigment analysis tool for hyperspectral images of cultural heritage artifacts", Proc. SPIE , Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, A ().

      A variational approach to hyperspectral image fusion A variational approach to hyperspectral image fusion Moeller, Michael; Wittman, Todd ABSTRACT There has been signiï¬ cant research on pan-sharpening multispectral imagery with a high resolution image, but there has been little work extending the procedure to high dimensional hyperspectral imagery. Abstract Contemporary and future high spectral resolution sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large vo Cited by: 3.

    RSC Publications & Proceedings (mostly Kruse) Hyperspectral, and Ultraspectral Imagery XIX, O ( ); , Expert system analysis of hyperspectral data: in Proceedings, SPIE Defense and Security, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, Conference DS43, 16 - 20 March Dr. Khan M. Iftekharuddin. SDL Author at Old Dominion Univ. SPIE Involvement: Machine learning, Image fusion, Image quality, Evolutionary algorithms, Telescopes, Optical design, Optical engineering, Aerospace engineering, Artificial intelligence, Detection and tracking algorithms Optics and Photonics for Information Processing X Showing.


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Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery X Download PDF EPUB FB2

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII. A classification approach and comparison to other object identification algorithms for hyperspectral imagery Comparison of minimum spanning tree reordering with bias-adjusted reordering for lossless compression of 3D ultraspectral sounder data.

Get this from a library. Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery X: April,Orlando, Florida, USA. [Sylvia S Shen; Paul E Lewis; Society of Photo-optical Instrumentation Engineers.;]. Get this from a library. Algorithms and technologies Algorithms and technologies for multispectral multispectral, hyperspectral, and ultraspectral imagery X: AprilOrlando, Florida, USA.

[Sylvia S Shen; Paul E Lewis; Society of Photo-optical Instrumentation Engineers.; SPIE Digital Library.;]. Arctic Sensor Networks - Softward Defined Multi-spectral Imager - Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIIAuthor: Sam Siewert.

RIS Citation TY - CPAPER ID - lau AU - LaVeigne, J. AU - Cairns, B. AU - Granneman, R. AU - Rael, A. PY - TI - Airborne Algorithms and technologies for multispectral BRDF measurements using the HyperSpecTIR instrument BT - Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, 12 Apr.

Orlando, Fla. T3 - Proc. SPIE VL - SP - DO - / ER. ANOMALY DETECTION IN HYPERSPECTRAL IMAGERY. In Proceedings of SPIEAlgorithms and Technologies for. Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Proc. SPIEAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, pg 1 (12 August ); doi: / Dr.

Alan P. Schaum. Physicist at US Naval Research Lab. SPIE Involvement: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV.

KEYWORDS: Target detection, Information fusion, Mathematical modeling, Coastal modeling, Detection and tracking algorithms, Data modeling, Opacity, Sensors, Target. An endmember is an idealized, pure signature that represents a spectral class. From the ground truth in Figure 1(b), 14 panel pixels, p 11, ppppppppp 12, p 22, p 32, p 42, and p 52 are considered to be pure pixels representing five distinct pure signatures.

Figure 2 shows the endmembers extracted using two popular endmember extraction algorithms. Multispectral imaging has also found use in document and painting analysis. Multispectral imaging measures light in a small number (typically 3 to 15) of spectral bands.

Hyperspectral imaging is a special case of spectral imaging where often hundreds of contiguous spectral bands are available. Plaza and C.-I Chang, "An improved N-FINDR algorithm in implementation," Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE Vol.

Orlando. SPIE Digital Library Proceedings. Proc. SPIEAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, (7 May ); doi: / SPIE Digital Library Proceedings.

Proc. SPIEAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, pg (23 September ); doi: / Hyperspectral imaging, like other spectral imaging, collects and processes information from across the electromagnetic goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.

In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, vol. p. International Society for Author: Thuy T.

Pham, M. Takalkar, M. Xu, Dinh Thai Hoang, H. Truong, Eryk Dutkiewicz, Stuart Perry. Abstract. In this paper, an automatic and efficient cloud detection algorithm for multi-spectral high spatial resolution images is proposed.

Based on the statistical properties and spectral properties on a large number of the imagery with cloud layers, a multispectral-based progressive optimal scheme for detecting clouds in Landsat-8 imagery is by: 3.

Spectral mixing is a problem inherent to remote sensing data and results in fewimage pixel spectra representing "pure" targets. Linear spectral mixture analysis isdesigned to address this problem and it assumes that the pixel-to-pixel variability in ascene results from varying proportions of spectral endmembers.

In this paper we present adifferent endmember-search algorithm called the Cited by: Alina Zare and Paul D. Gader, “L1-endmembers: a robust endmember detection and spectral unmixing algorithm”, Proceedings of the SPIE Conference Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, AprilCDROM.

The students, research staff, and faculty of the Digital Imaging and Remote Sensing laboratory prolifically exhibit their world-class research at all of the major international remote sensing conferences and in the top-tiered discipline-specific journals.

Proc. SPIEAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, (2 July ); doi: /. Kruse, F. A.,Expert system analysis of hyperspectral data: in Proceedings, SPIE Defense and Security, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, Conference DS43, 16 - 20 MarchOrlando World Center Marriott Resort and Convention Center, Orlando, Florida, USA, Paper Number: InDr.

Velez-Reyes was elected Fellow of SPIE for his contributions to hyperspectral remote sensing. He is a senior member of the IEEE. Dr. Velez-Reyes is the Chair of the SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery.Classifying hyperspectral data within high dimensionality is a challenging task.

To cope with this issue, this study implements a semi-supervised multi-kernel class consistency regulariser graph-based spatial–spectral feature learning framework.

For feature learning process, establishing the neighbouring relationship between the distinct samples from the high-dimensional space is the key to Cited by: 8.