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25 Acquisition and Mosaicking of Large / High-Resolution Multi- / Hyper-Spectral Images PAOLO PELLEGRI, GIANLUCA NOVATI DISCO - DIPARTIMENTO DI INFORMATICA, SISTEMISTICA E COMUNICAZIONE UNIVERSITÀ DEGLI STUDI DI MILANO – BICOCCA Via Bicocca degli Arcimboldi, 8 – 20126, Milano - Italy [novati, pellegri]@disco.unimib.it AND ITC, ITALIAN NATIONAL RESEARCH COUNCIL Via Bassini, 15 – 20133, Milano – Italy Phone: +39-02-23699-559, Fax: +39-02-23699-543 [novati, pellegri]@itc.cnr.it RAIMONDO SCHETTINI DISCO - DIPARTIMENTO DI INFORMATICA, SISTEMISTICA E COMUNICAZIONE UNIVERSITÀ DEGLI STUDI DI MILANO – BICOCCA Via Bicocca degli Arcimboldi, 8 – 20126, Milano - Italy Phone. +39-02-64487840, Fax +39-02-64487839 schettini@disco.unimib.it 1. Introduction In recent years, the limitations of ‘traditional’ RGB imaging have become more and more evident as the requirements in terms of image quality are being raised, and new uses and applications are being conceived within the digital imaging field. At the same time, multispectral imaging has been emerging as a technology that allows the acquisition of color images with a superior quality [1], and has established itself as a suitable alternative to (and evolution for) current RGB imaging. A number of studies have already been published that outline the general framework of multispectral imaging [2-5] as well as more specific technical issues [6,2]. Potential applications are plentiful and are actively being investigated, and several prototype multispectral acquisition systems have been assembled in research labs all over the world. However, so far multispectral imaging has generally been seen as a cutting edge technology that can yield high quality results but is accessible only to resourceful institutions and companies. In this paper, we briefly introduce the theoretical framework of multispectral imaging, particularly with regard to 'wide-band' multispectral acquisition. In doing so, we drop any distinction (usually based on the number of bands considered) between hyper- and multispectral imaging, and only use the latter term. We then introduce a system assembled by authors for the acquisition of large / high-
esolution multispectral images, and outline the methodology employed to operate it. 2. ‘Narrow-band’ multispectral imaging Compared to RGB imaging, which is based on the theoretical framework of colorimetry [7] and therefore ‘synthetizes’ color stimuli from the contributions of objects, environment, and observer, multispectral imaging attempts to estimate objects’ reflectances. It is therefore unaffected by the typical problems of RGB imaging, including device-dependency [1], metamerism [7,1], and accuracy limitations of the device sensors [8,9]. In fact, despite the availability of colorimetric device-independent color spaces and international standards such as ICC profiles [10] and sRGB [11], multispectral imaging remains the only way to achieve complete independence from both the environment and observer. Motivations for the use of multispectral imaging can be found in everyday experiences like the phenomenon of metamerism, which shows that there exist different ‘physical’colors (spectra) that sometimes get the same colorimetric representation. At the same time, physics outlines that while color representations in RGB imaging and colorimetry use parameters whose ultimate physical significance is that of measuring the amount of light energy that is ‘registered’ by the sensors considered (both human and electronic), such parameters have only an indirect relationship with the fact that the objects observed are actually able to reflect light towards those sensors. The aim of multispectral imaging is then that of describing this ‘ability’ of color surfaces as modelled by their reflectance function; as this function depends on the physical properties of the surfaces considered, it is also much more invariant than environmental conditions and observers sensitivity, and therefore more ‘fundamental’. In general, the acquisition performed using a given sensor will return a value a in the form (1) = E( ) R( λ) S( λ) 26 λ 2 ∫ a λ dλ . λ 1 This value integrates contributions from the energy E that reaches the physical sample observed, the color reflectance R of the sample, and the ‘sensitivity’ S of sensor. The integration with respect to the wavelength λ is performed in the range λ1 to λ2 of the sensor's sensitivity; if this range exceeds that of the visible light spectrum, then appropriate steps must be taken to cut unwanted radiation off. To obtain an estimation of the reflectance R, two different approaches are currently used in multispectral imaging [12]. On one hand, direct measures of these values can be attempted if the device’s sensors are sensitive to a very narrow wavelength
Page 1 and 2: SOCIETÀ ITALIANA DI OTTICA E FOTON
Page 3 and 4: Proposta di una stazione a camera d
Page 5 and 6: icostruire il fattore di riflession
Page 7 and 8: costruttive. In ambito applicativo
Page 9 and 10: quindi fondamento nel salvaguardare
Page 11 and 12: e, raggiungibile e controllabile co
Page 13 and 14: Tale ricerca di solidità è atta a
Page 15 and 16: Scanner ottico iperspettrale per la
Page 17 and 18: - R( λi ) è il fattore di rifless
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Page 21 and 22: consentendo la ripresa di immagini
Page 23 and 24: Fig. 5 - Immagine dell’interfacci
Page 25 and 26: Fig. 6 - Fattore di riflessione spe
Page 27 and 28: perpendicolare e parallela alla dir
Page 29: 3. J. Y. Hardeberg, F. Schmitt, H.
Page 33 and 34: function R. In mathematical terms,
Page 35 and 36: in standard and professional photog
Page 37 and 38: The acquisition output vectors are
Page 39 and 40: and then ‘stitched together’ to
Page 41 and 42: 36 Template matching Fig. 6 - A sem
Page 43 and 44: References 1. Fairchild M.D., Rosen
Page 45 and 46: 40 Sistema stereoscopico di visione
Page 47 and 48: macroscopiche sullo stato di conser
Page 49 and 50: 44 Fig. 1 - Il sistema SVA Due di e
Page 51 and 52: Si pone un piano di calibrazione, f
Page 53 and 54: a 20 000 K. I valori corrispondenti
Page 55 and 56: 50 Tabella 1 Colore LSVA xSVA ySVA
Page 57 and 58: 52 Spectral-based printer modeling
Page 59 and 60: 2. Printer Model The model of the p
Page 61 and 62: covered is computed as the differen
Page 63 and 64: The spectra were measured with a Gr
Page 65 and 66: 60 Spettrofotometro a scansione per
Page 67 and 68: icostruire il profilo tridimensiona
Page 69 and 70: dalla fibra è a sezione circolare.
Page 71 and 72: con maniglie e ruote per semplifica
Page 73 and 74: In fig. 5 sono riportati i risultat
Page 75 and 76: 70 380nm….430nm….470nm…510nm
Page 77 and 78: inciderebbero sulla forma del fatto
Page 79 and 80: Infine in Fig. 9 sono riportati i r
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2. D. Anglos, C. Balas, C. Fotakis,
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78 Fig. 1 - Filtri interferenziali
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E’ chiaro tuttavia che uno strume
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scientifici e di restauro è affian
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Per quanto riguarda la precisione d
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immagini componenti principali (aut
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6. A. Casini, F. Lotti, M. Picollo,
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� necessità di una movimentazion
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92 Trasmittanza % 90 80 70 60 50 40
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lungo l’asse di scansione. Con lo
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I valori di Riflettanza (R T % e R
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Minolta. Una matrice di corrisponde
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Tab. 3 - Confronto in termini di Δ
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Bibliografia 1. Fermi F., Antonioli
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2. Quali diagnostiche per l’Arte
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106 Fig. 3 - Rappresentazione delle
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e tre i riquadri, anche in quelli c
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Fig. 8 - PCA delle misure rilevate
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Colorimetria multispettrale per la
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stanno alla base dell’agricoltura
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116 Immagini aeree o da satellite M
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Non solo sono identificate per ogni
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vigneto, e non si sofferma certamen
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1. Introduzione 122 Spettrometria d
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Negli anni successivi, mentre veniv
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126 Fig. 5 Il software comanda poi
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Nei colori a basso contrasto, l’o
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130 Proposta di una stazione a came
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Fig. 2 − Singola immagine cattura
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134 400 500 600 700 nm lunghezza d
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6. dal confronto di misure di coppi
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Bibliografia 1. P.C. Hung, “Color
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movimento. Se infatti il filtro ste
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142 a b Fig. 2 - Trasmittanza di un
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Fig. 5 - Trasmittanza teorica di un
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dimensione della memoria di bordo p
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148 Estimating Surface Reflectance
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150 ~ ∑ j= = N w R( λ ) ( λ) (3
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R( λ) = E ⎡ 2 ⎤ ⎡ 2 ⎛ λ
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Tab. 1 - Statistics of results obta
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13. D.E. Goldberg, Genetic Algorith
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sono affetti da grandezze di influe
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160 Output(mV) 2500 2000 1500 1000
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Nel seguito del documento questa co
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appresentate nel grafico della figu
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A 166 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0
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3. Joao Carlos de Andrade, Jiulio C
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2. Generalità Richiamiamo brevemen
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spettrometro al CCD della camera. N
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Fig. 7 - Immagine fornita dalla cam
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profilo di intensità spaziale del
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Fig. 12 - Stabilità del segnale di
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9. Conclusioni Abbiamo cercato di a
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Fig. 1 - Vista parziale del MIR. Si
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pixel della matrice CCD. Purtroppo
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Determinare un riferimento dimensio
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desiderata per l’immagine finale.
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conoscono le caratteristiche spettr
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Scrovegni di Padova. Da questa anal
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