suitability of the dcs460c colour digital camera for quantitative remote sensing analysis of...

Ž .ISPRS Journal of Photogrammetry & Remote Sensing 55 2000 105–118www.elsevier.nlrlocaterisprsjprs

Suitability of the DCS460c colour digital camera for quantitativeremote sensing analysis of vegetation

Christopher Dean a,), Timothy A. Warner a, James B. McGraw b

a Department of Geology and Geography, West Virginia UniÕersity, PO Box 6300, Morgantown, WV 26506-6300, USAb Department of Biology, West Virginia UniÕersity, PO Box 6057, Morgantown, WV 26506-6057, USA

Abstract

The Kodak DCS460c is a high resolution, colour digital camera with a specific CCD array mosaic structure that requiresŽ .a dedicated processing algorithm for production of a three-band image. Kodak’s Active Interpolation KAI algorithm is

Ž .compared with a simpler nearest neighbour interpolation method NNI with regard to applicability in scientific investiga-tions of target features in imagery. Detailed quantitative analyses of flat-field imagery were undertaken in order to provideinsight into artefacts and to correct for the off nadir reduction in brightness. Characteristics of the aerial photography werealso studied and aspects of processing and analysis of the imagery for scientific investigations are discussed. We present a

Ž .novel method for correction of brightness fall-off due to the vignette effects and increasing view angle which is generallyapplicable to remote sensing of rural scenes. Quantitative spectral comparisons between related imagery from the DCS460cŽ . Ž .e.g. for interpretation of relative vegetation cover are only scientifically reliable when the raw i.e. non-interpolated DNvalues for green and red are below about 67% of their maximum range. This is due to preferential vertical leakage or chargediffusion in the CCD. A combination of CCD characteristics leads to possibly poor spatial resolution for the infrared band.The imagery after KAI shows good spatial resolution and has a natural looking colour reproduction useful for discerningfeatures by eye. However, when using KAI, reliable spectral measurements can probably only be acquired from portions ofobjects that are at least four pixels from the object’s boundary. In general, quantitative spatial and spectral analyses can becarried out with the DCS460c but the KAI, the broad band response and the vertical leakage require specific solutions toproduce quantitative results. q 2000 Elsevier Science B.V. All rights reserved.

Keywords: digital camera; forest; aerial imagery; multispectral; colour-mosaic CCDs; colour interpolation; radiometric corrections

1. Introduction

With recent improvements in digital cameras, dig-ital imaging is becoming a standard data acquisition

) Corresponding author. Present address: GISCA, The Univer-sity of Adelaide, Adelaide, SA 5005, Australia. Tel.: q61-8-83033900; fax: q61-8-83033498.

Ž .E-mail addresses: [email protected] C. Dean ,Ž . Ž[email protected] T.A. Warner , [email protected] J.B. Mc-

.Graw .

method for scientific and engineering applications.One digital camera, which has been used in a varietyof applications, is the Kodak DCS460c. It has beenemployed in various aerial camera systems and usedsuccessfully for routine remote sensing applicationsŽe.g. Petrie, 2000; Positive Systems, 2000; Sen-sytech, 2000; NAR, 1999; Compass Informatics,

.1999 and for industrial inspection and large-scaleŽ .engineering metrology Shortis and Beyer, 1997 .

Ž .Mason et al. 1997b used the camera for remotesensing of urban settings. Cooled versions of the

0924-2716r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved.Ž .PII: S0924-2716 00 00011-3

( )C. Dean et al.r ISPRS Journal of Photogrammetry & Remote Sensing 55 2000 105–118106

same CCD array are used in other cameras forŽphotomicrography Technical Instrument San Fran-

. Ž .cisco, 2000 and astronomy EuroPixel, 1998 . How-ever, it seems apparent that the DCS460c was de-signed for photojournalism applications rather thanfor quantitative analysis in scientific or engineering

Žapplications e.g. Shortis and Beyer, 1997; and Stow,.personal communication . The non-quantitative as-

Ž .pect of the camera’s design is reflected in: a methodŽ .of colour estimation in post-processing software; b

the flexible fixture of the CCD array within theŽ .camera; and c its portable design and use of a

Žfamiliar 35-mm camera body. Some resellers e.g..Computer Imageworks, 1997 nevertheless adver-

tised the camera as being suitable for scientific in-Ž .vestigations. Mason et al. 1997a reported that the

DCS460c was suitable for photogrammetric work,although they qualified this by pointing out the poorradiometric quality and the slight loss of resolutiondue to hardware structure and colour interpolation.Additionally, Kodak now manufactures and pro-motes instead the Kodak EOS DCS-1 with Nikonparts replaced by Canon. However, the DCS460c isstill on the market.

Our acquisition of an ADAR 1000 camera systemŽ .with the DCS460c at the centre of the system andintention to use it as a remote sensing deviceprompted us to investigate the suitability of imageryfrom the DCS460c for our purposes. Our aim is tofind practical methods of ascertaining individual tree

Ž .attributes species, size and health etc. over largeareas of forest using aerial photography. One aid tospecies identification in deciduous forests is the phe-nological characterisation of the trees by collecting

Ž .multi-temporal data images at various dates anddetecting the spectral changes. The data from aerialimagery would initially be checked against ground-based data collection consisting of individual treespecies and crown identification. Previous work in

Ž .this area used 35-mm photography Key et al., 1998Ž .but the procedures were problematic due to: a the

degree of care needed for the CIR film and its cost;Ž . Ž .b inconsistency in film development; and c errorsintroduced during scanning. Digital photography by-passes some of these problems because the data arealready in digital form. Also the CCD pixels aregenerally observed to have a linear intensity re-

Ž .sponse to radiant intensity e.g. Stow et al., 1996 ,

whereas the response of film is logarithmic. There-fore, spectral calibration between different datesshould be more tractable in digital imaging. For thetree delineation part of our aerial photography exper-iments, we require that the spatial features are im-

Žaged without significant artefacts features not due to.the target from image processing and that they are

correctly located. For the spectral part of the experi-Ž .ments, we require that the wavelength spectral

characteristics of the processed pixels are identifiableand can be standardised between images. While wewere interested in these aspects for our remote sens-ing application, other scientific applications wouldalso likely benefit from these potential advantages ofdigital imaging. These aspects of the imagery fromthe DCS460c were investigated in this work.

2. Apparatus and study area

The aerial imagery collection system was thePositive Systems ADAR System 1000 which primar-ily consisted of a Kodak DCS460c digital camera

Ž . Žwith visual colour VIS and CIR with a 650BP300.299 9711 filter 20-mm focal length lenses. The

camera’s CCD array is a KAF-6300 chip. The othermain part of the ADAR System 1000 was controllingsoftware installed on a dedicated PC NT, mounted inthe aeroplane. The plane used was a specially adaptedPiper Apache, with the camera mounted in the basalfuselage between the wings. Differential GPS wasused for navigation and locating ground controlpoints. A Silicon Graphics SGI-320 PC with a Pen-tium II 450 MHz processor, 512MB RAM and run-ning Microsoft Windows NT was used for imageprocessing and analysis. Programming was per-formed using Cygnus Solution’s Cygwin and theGNU Cqcompiler, making software readily portableto Unix.

The transmission spectra of the lenses are shownin Fig. 1. Data were collected early one cloud-freeafternoon in July 1999 with moderate summer haze,conditions typical of summer flight data collections.The spectrometer was an Analytical Spectral DevicesŽ .ASD Full Range Spectrometer, covering the region400–2500 nm. The amplitudes were not normalised

Ž .for the unit area of incident light sunlight on thefront of the lenses; consequently the curves in Fig. 1

( )C. Dean et al.r ISPRS Journal of Photogrammetry & Remote Sensing 55 2000 105–118 107

Fig. 1. Relative transmission spectra of the VIS and CIR lenses.

are relative. The spectral graphs show that there aredistinct non-transmission wavelengths for bothlenses. The VIS lens has an IR filter and transmits

Ž .fairly evenly across the red around 630 nm , greenŽ . Ž .around 510 nm and blue around 450 nm regionsbut with a little less blue being transmitted. The CIRlens and filter combination does not transmit bluelight; it transmits fairly evenly across the red andgreen but transmission slowly decreases in the near-IR.

Digital image data of the study area was routinelycollected at a frequency of about once per fortnightduring summer, spring and autumn. The study site

Ž .was a 300=200 m area 6 ha of secondary growthmixed deciduous forest in central Appalachia, ap-proximately 15 km east of Morgantown, WV, USA.The study area contains a small valley, has a meanelevation of about 600 m, 0–458 slopes and theaverage aspect is southerly. Images were taken assingle exposures, while flying an average altitude ofabout 465 m above the study site, yielding imagepixel widths of about 0.2 m. The camera settingswere held constant with a field stop of 2.8 and anexposure duration of either 1r500th s or 1r1000th s.Each flight included several passes over the site,producing a success rate of about 3 images of theentire study area per lens per flight. Images werestored on the ADAR PC in a Kodak proprietaryTIFF format and after each flight they were down-

loaded to the SGI PC. The GPS data for the flightwas also downloaded. Each TIFF file included anapproximate time of exposure that could be usedlater in conjunction with the stored flight path file todetermine the location of the camera for each expo-sure.

Luminance and spectral calibration targets wereinstalled next to the study site. Initially, two differentcoloured tarpaulins and permanent road markingswere used prior to the permanent installation of threepainted plywood panels. The wooden panels weresquare with a surface area of 6 m2 and painteddifferent shades of grey. The spectral calibrationtargets together with other panels distributed withinthe study area and around its perimeter aided ingeorectification.

3. Data interpretation and preliminary processing

The KAF-6300 array consists of 2048=3072pixels, each 9=9 mm2. The pixels are coated with

Žfilters to produce a Bayer colour filter array Bayer,.1976 that has twice as many green pixels as red or

blue. The layout of the array is shown in Fig. 2.During image processing by Kodak software, the

six rows and columns of pixels around the edge ofthe array, which Kodak believe to have low quality,are discarded, leaving a 2036=3060 array. SomeDCS460 cameras have anti-blooming circuitry toreduce the effects of photoelectron overflow whenimaging bright targets. We could not determine ifour camera had such circuitry, likewise the defect

Fig. 2. Arrangement of colour filters on sensor elements of theDCS460c CCD.


rating for our particular DCS460c camera could notbe ascertained. Defects, which can change with time,cause pixels to give anomalous signals.

In the images obtained from the camera, eachŽ .pixel is represented by a digital number DN from 0

Žto 255 in one of either red, green or blue or red,.green, near-infrared, if using the CIR lens . After

processing each pixel location is represented by indi-Žvidual red, green and blue values or red, green and

.infrared again from 0 to 255. We computed theother two colours for each pixel by simply taking theaverages of the nearest appropriately coloured neigh-

Ž .bours. For example: 1 the green value for anŽ .innately red or blue pixel is obtained by averaging

Ž .the green pixels on each of its four sides; 2 the redvalue for an innately green pixel in an odd-numbered‘‘red column’’ is obtained by averaging the two redpixels immediately above and below it. We will refer

Žto this method as ‘‘NNI’’ nearest neighbour interpo-.lation .

A more complex method to create the three bandsfor images is used by Kodak ‘‘DCS-Access’’ soft-

Žware and it is described in a series of patents e.g.Hamilton and Adams, 1997; Adams and Hamilton,

.1997 . Their method takes into account the spectralresponse curve of the CCD array, as well as spectraof the expected incident light, with options for‘‘daylight’’, tungsten or fluorescent lamps, ‘‘none’’Ži.e. incident light is considered to be spectrally

. Žbalanced and ‘‘click’’ where a user-designated spotin the image is regarded as representative of the

.incident light . The Kodak algorithm dynamicallyselects which nearby pixels are suitable for use inestimations. For example, if an edge of a suspectedobject is close to the pixel for which colours arebeing calculated then only pixels on the near side ofthat edge are used. Consequently, the Kodak interpo-lation method is referred to as ‘‘Active Inter-polation’’, abbreviated to ‘‘KAI’’ in this manuscript.In the KAI some Laplacian second order values,gradient values and colour difference bias values areused and the calculations can be recursive to acertain level. The DCS-Access software is suppliedas part of the ADAR System 1000 package and weunderstand that this software has changed periodi-cally. Kodak considers which particular version ofthe KAI methodology was used in our copy ofDCS-Access proprietary information and conse-

quently we are unable to present that informationhere.

4. Corrections for brightness fall-off with viewangle

There are several features of digital photographywhich are due to the method in which light from thetarget is imaged onto the CCD array and thesefeatures are best removed by a ‘‘correction’’ proce-

Ž .dure e.g. Pellikka, 1998 . One of the strongestartefacts in our imagery was that brightness de-

Žcreased significantly towards the corners e.g. in.Fig. 3b . This radial reduction in intensity was partly

Ž .Fig. 3. Green band images of a the lens cap through the VIS lensŽ . Ž .i.e. dark noise and b a sheet of red paper but with no lens. The

Ž .lower half of b shows a cross-sectional profile for the threebands.


due to the increasing obliquity in the view awayfrom the nadir axis and partly due to vignette effectsfrom the lenses. For the sake of simplicity, in the restof this article, we refer to the brightness fall-off as‘‘vignette effects’’, even though it includes view-an-gle obliquity effects. One method of vignette correc-tion is to develop a radial trigonometric power func-

Ž .tion e.g. Nixon, 1996; Pellikka, 1998 to counteractthe fall-off in intensity away from nadir.

Other researchers have used a more specific vi-gnette correction method by using ‘‘flat-field im-

Žagery’’ from a monochrome, uniformly illuminatedtarget such as an integrating sphere, white panel or a

.diffuser plate to produce a vignette effects templateimage that is inverted and multiplied with all subse-quent imagery. How well this flat-field imagery rep-resents vignette effects is uncertain but others sug-

Žgest that it is a reliable method e.g. Nguyen et al.,.1996 . Positive Systems acquired two such flat-field

Ž .images one for each lens using our camera, set atfield stop 2.8 for a total cost of US$755. The target

Ž .was a ‘‘Fostec’’ Schott-Fostec, 1999 fibre-optic‘‘backlight’’ and about 40 images of it in differentorientations were taken and averaged into one image;this was done with each lens. The two resultingimages from the Fostec backlight will be referred to

Ž .as ‘‘Fostec templates’’ for simplicity to signifytheir origin and function. The Fostec templates forthe VIS and CIR lenses are ‘‘v fostec’’ and ‘‘c– –fostec’’, respectively. The Fostec templates had low

Žlevel vertical striations probably due to fixed pattern.noise . For a description of types of noise see, e.g.,

Ž .Silicon Mountain Design 1998 . Also there wereripples towards the corners due to the integer rather

Žthan floating point format of the templates combin-ing bands with integer steps produced variations in

.colour . Supporting the observation of lack of sym-metry in the imagery is that the dark noise imageŽ .Fig. 3a , acquired with the lens cap in place, re-vealed both random fluctuations plus horizontallydistinct regions. The DN values across the image inFig. 3a range from 20 to 25 with pixels on the farleft having a value of 20 only. Similar amounts offluctuation were seen in the illuminated noise imageŽ .Fig. 3b, flat field taken without the lens but itappeared to be more vertically structured. For min-imisation of the high frequency noise, the Fostectemplates were filtered using a method similar to

Ž .those of Nguyen et al., 1996 and of Stow et al.,Ž .1996 . However, rather than using a single 2D filter,the filtering was separated into angular and radial

Ž .components described below in order to retainmore of the radial structure of the vignette effect.

Ž .Positive Systems personal communication has aproprietary method for applying vignette effect cor-rections to monochrome imagery using similar Fos-tec templates but it was not found satisfactory forour colour imagery because it introduced changes inhue, especially in the corners of the image. Thecorrection method developed for this project includesfiltering, then scaling, using estimates of both themaxima and minima of the templates and sampleimages.

The first step in the filtering of the Fostec tem-plate is to make an average cross-sectional profile ofeach band, with the abscissa of the profile being aline from the centre of the image to a corner and theordinate being the DN. The DN are stored in floatingpoint format in bins along the abscissa, half a pixelwide. The values in the bins are then smoothed alongthe radial direction using a weighted mean, within amoving window. The weighting is inversely propor-tional to the distance of the sample pixel from thepixel of interest. Typically the window width is set at10 pixels and the entire profile is smoothed fivetimes. Those parameters were empirically found toachieve the minimum amount of radial smoothingnecessary to remove high frequency noise from the

Žprofile of the v fostec template result shown in–.Fig. 4 . The parameters can be increased for viewing

the smoothed profiles of aerial images which typi-Žcally have more low frequency spatial variation e.g.

.from different trees than the Fostec templates. Theprofile of each band is then rotated through 360o tocreate a full image, in floating point format.

The maxima of the templates are measured fromthe pixels within a central circle and the minima aremeasured from an outer ring of the expanded profile.For flat-field images both circular areas typicallyspan 50 pixels radially but this can be increased foraerial images. If the inner or outer zones are notrepresentative of the area of interest within the imagethen artefacts will occur in the form of unnaturalhues or intensities. For example, in a forested scene,if the centre of the image contains a bright, non-col-

Ž .oured grey , wide road, then the correction process


Fig. 4. Cross-sectional profile of bands in v fostec after filtering–Ž .angular averaging and radial smoothing . Upper and lower curves

Žfor each band represent one standard deviation for the averaged,.non-smoothed data .

will over-enhance the red and blue DN values of thevegetation. To accommodate roads in our images ouralgorithm allows the user to select a representative

Žsector of the image e.g. western half or NE quad-.rant from which to select representative values for

the minima and maxima. The degree of fall-offshown in the Fostec templates is corrected for in thesample image, by calculating for each pixel in theFostec template, a reduction in brightness from max-imum intensity, as a fraction of the maximum reduc-tion. That fraction is then scaled according to themaximum range in brightness for the sample imageand the result is added to the sample pixel value.This was done for each band in each pixel. The

Žcorrected sample pixel DN for a particular band red,. Xgreen or bluerIR is therefore given by S where:i j

SX sS q c yo = c yF r c yoŽ . Ž .Ž .Ž .ž /i j i j s s F i j F F

1Ž .

where the variables are: SX scorrected sample DNi j

for pixel in row i and column j; S suncorrectedi j

sample DN for pixel in row i and column j; F si j

smoothed Fostec template DN for pixel in row i andcolumn j; o saverage DN within an outer ring ofF

the smoothed Fostec template; c saverage DN fromF

within a central circle in the Fostec template; c sS

average DN from within a central circle in thesample; o saverage DN within an outer ring of theS

sample.

The images corrected for vignette effects usingEq. 1 were much more consistent than without usingindividual band minima, both within a single imageand between images. Also, there were far fewerripples in the hue when using a smoothed Fostectemplate in floating point form than without smooth-ing the template. In order to completely avoid ripplesin hue it was necessary to keep data in floating pointformat rather than to write intermediate integer for-mat files to disk. Consequently the intensity, hue,

Ž .saturation IHS image is calculated within the sameprogram used to perform the vignette. Even so, afterKAI, very faint ripples of DN in the blue band werepresent in some images. This most probably resultedfrom the DCS-Access software requiring the rawŽ .integer format for input and the integer steps aremagnified when the blue band is stretched by KAI. Itwas observed there was no difference in the finalimages if NNI was applied before the vignette cor-rection compared to applying NNI after the vignettecorrection. However, when using KAI it was neces-sary to apply the vignette correction before the KAI,otherwise the hues became incorrectly adjusted dur-ing the vignette correction.

The vignette effect is known to be different inŽ .degree for different wavelengths e.g. Nguyen . In

the v fostec template the red band has a more–uniform fall-off than the green band, the latter hav-ing a lower rate of fall-off near the centre but a

Žgreater rate near the edge of the field of view Fig..5a . The bands in c fostec did not show such a–

pronounced difference in vignette effects as for v–fostec because each band in CIR images each has alarge component of a common near-IR influence.Curves of the quantum efficiency of the CCD versus

Ž .wavelength Shipman, personal communicationshow that our camera has broad band responsivenessŽe.g. the green band includes a significant response

.to red light and vice-versa . The broad band responsecombined with wavelength dependent fall-off causethe vignette effects for each band to vary with thetarget scene. The difference in the vignette effect of

Ž .the red band for different targets Fig. 5b gives anindication that, as the fraction of green light comingfrom the target increases, the vignette effect for thered band tends more towards that of the green band.Therefore, the choice of the vignette mask to be usedwith VIS images from the DCS460c should depend


Ž .Fig. 5. Cross-sectional profiles after filtering of a v fostec after–Ž .scaling green and blue bands to the red one and b red bands for

different targets after scaling to the red band of v fostec.–

on the predominant colour of the target image. Thesmoothed profiles for the v fostec template and for–

Žthe lush green forest sample appear very similar Fig..5b . And indeed close examination of the RGB and

IHS images for that forest scene were corrected forvignette effects at least as accurately and preciselywhen using the smoothed profile of that same forestscene as when using the v fostec template. For–autumnal images the use of the sample image itselfto create a vignette template also produced a simi-larly accurate vignette correction even though the

trees were a mixture of green, orange and red; thesmoothing window was increased to 100 pixels tocater for that variation.

5. Fine scale features in imagery, independent oftarget texture

5.1. Fostec back-light flat-field imagery

The most obvious fine scale artefact was stripingin the green values. The Fostec templates were anal-ysed to characterise this artefact because they hadthe least variations. For visual analysis the raw pro-prietary TIFF images were converted into greyscalestandard format TIFF files using ImageMagick’s

Ž .‘‘convert’’ utility Du Pont de Nemours et al., 1999 .Relatively radiometrically flat areas were analysed.An area of the image was considered ‘‘flat’’ if every

Žsecond green pixel green pixels in the columns with.alternating green and blue pixels along a diagonal

Žhad equal DN, and if the interleaving pixels greenpixels in the red columns with alternating green and

.red pixels were also equal. No requirement wasplaced on any relationship between these two sets ofoverlapping pairs. The width of a ‘‘flat’’ diagonalwas considered to be at least five green pixels wide,diagonally and the length of a ‘‘flat’’ diagonal wasfive or more green pixels. Profiles across a flat areaof the v fostec image help to illustrate the variation–present. The profiles after processing by NNI areshown in Fig. 6a and profiles after processing byKAI using ‘‘daylight’’ compensation are shown inFig. 6b. The latter figure shows that in KAI the

Ž .neighbouring pixels of a different band have influ-enced the otherwise regular pattern of green varia-tion and that in turn the green variation has affectedthe red band.

The KAI of red and blue data for the green pixelsalso results in some features that appear to be arte-facts, and are therefore undesirable for our purposes.Consequently, the spectral properties of a pixel afterKAI can depend on the intensity of its neighbours.Additionally, during KAI the mean value of eachband is shifted. In Fig 6b, the blue was reduced tozero and the red was increased from 110 to 210 DN.This extreme band shifting can be avoided by using


Fig. 6. Profiles through bands in a radiometrically flat area inŽ . Ž .v fostec. The profiles are after interpolation with a NNI and b–

KAI with daylight compensation. Both profiles follow the samew x Ž w xdiagonal line starting at 844,1828 s 850,1834 in the raw

.image .

‘‘click’’ compensation but that still shifts all bands,which is not appropriate for use in quantitative anal-ysis.

Comparisons of the fluctuation along the greendiagonals against average green values and against

Žred-minus-blue values where the red and blue val-ues are from neighbouring diagonals in the raw

.imagery both showed high correlation. When theaverage DN values were higher, the fluctuation alongthe green diagonals appeared to be also higher andthe difference between neighbouring red and bluewithin that relatively flat diagonal is also higher.

The CIR images showed a larger range in thefluctuation of the green values compared to the VISimages. The CIR values for red-minus-blue are alsohigher, because ‘‘blue’’ pixels record only infraredwavelengths with the minus blue filter of the CIRlens.

Simple bispectral plots of the data from diagonalsin radiometrically relatively flat areas of the Fostectemplates showed that the bands were highly corre-lated. For example a graph of red-minus-blue versusaverage green showed a mostly straight line but withcurvature at the lower end corresponding to reducedresponse at the edges of the image.

5.2. Other flat-field imagery

Further analysis of flat-field imagery was doneusing coloured sheets of laser printer paper; blue,red, orange and white. Those colours have greatercontrast between the non-green bands and shouldtherefore reveal colour discrimination effects better.Thickness of paper ranged from 40 grm2 for theblue to 200 grm2 for the red paper. To prevent otherlight entering the camera and to prevent focussing ofthe papers’ structure the sheets were placed flushagainst the lens. The paper targets consisted of twoor more sheets arranged askew and rapidly movedduring exposure so as to reduce observed detail. Thelight source behind the paper was daylight.

The most obvious results from the orange and redtargets were much larger red-minus-blue values andlarger green fluctuation along the diagonals thanwith the Fostec templates. Fig. 7 shows the typicalspread of data obtained from the flat-field imageryand a collection of such graphs in reduced form isshown in Fig. 8.

From Fig. 8 it is evident that the red-minus-bluedifference, at medium to lower green values, in-creases almost linearly with green and in combina-tion with the high correlation, this causes the fluctua-tion along the green diagonals to similarly increase.When either red or green DN values are above about

Ž .Fig. 7. Red-minus-blue DN of radiometrically flat areas in thev red3 target. Each flat area was at least 5=10 green pixels in–size. There are 3,113 such flat areas represented in this graph.


Ž .Fig. 8. Red-minus-blue DN of radiometrically flat areas inflat-field images. Each data point on each curve is an average of

Ž .several flat areas anywhere from 1 to 3,400 .

170 DN then the linear relationship between red-blueand green, and between red and green is lost and thecurves have a parabolic-shaped maximum. After red

Ž .is saturated at 255 DN blue continues to increasecausing a downward linear slope.

The fluctuation in green values along diagonalsappears to be caused by the DN values of thevertically neighbouring pixels. Red has higher DNthan blue in most of the flat-field imagery and it isobserved that green pixels in columns with red pixelshave a higher DN than do green pixels in the columnswith blue pixels. It is known that there is preferentialleakage in the CCD in the columnar direction, due tothe structure of the chip necessary to accommodatethe ‘‘bucket brigade’’ electronics and the vertical

1 Ž .channel stops Adams, personal communication .This accounts for both the fluctuation in green DNand the loss of the linear correlation between bandsat higher DN. However, to the best of the authors’knowledge, that vertical leakage has not been quanti-tatively analysed in imagery before this present work.Another possible cause could be a vertical irregular-ity in the placement of the colour filters over theCCD pixels but that might give a more gradualvertical influence of neighbouring pixels. For thev blue1 image, wherever blue DN was higher than–

1 Channel stops are designed to minimise diffusion of photo-Ž .electrons between pixels Taylor, 1998 .

red, the green values in the columns with blue pixelswere higher than the green values in columns withred pixels. The relationship was also found in reversewhen red DN were higher than blue DN. This obser-vation strengthens the notion that intensities ofneighbouring pixels are related by vertical leakage.

Graphs of blue DN versus green DN showed nofall-off as blue approached saturation because thevalues were not initially recorded at that level, theyhad been electronically intensified to compensate for

Žlow blue response of the CCD Adams, personal.communication . The blue detectors are much less

sensitive than the other wavelength detectors partlybecause of the lower inherent detector efficiency forblue wavelengths, as well as the metal detector gateŽ .on the incident light side of the silicon substrateabsorbing shorter wavelengths much more stronglyŽ .Suni, 1995 .

6. Fine scale features in the imagery, dependenton target texture

Flaring around bright objects was investigatedusing the 6 m2 spectral calibration targets. The ex-cessive flaring apparent in the left-most panel in Fig.9 appears to be particularly associated with the greenband saturation and therefore it may be due toblooming effects.

Fig. 9. Spectral profile through calibration panels in a VIS imageŽ .taken in July 1999 NNI applied . Each panel is 2.44=2.44 m.


Part of the shadow of the middle panel is ob-scured, thereby suggesting flaring from this panel,even though the green band reaches only 225 DN. Itis uncertain whether this flaring is due to CCDeffects but in any case it indicates that pixels nearthose with DN of 225 and above should only be usedwith reservations. The flaring in these spectral cali-bration panels led us to install darker ones for spec-tral calibration and for georeferencing.

The KAI of the green band shows the edges ofobjects a little more sharply than with our nearestneighbour averaging. This is shown in Figs. 10 and11. Closer examination showed that the sharpeninghas occurred within a distance of about three pixels.The KAI has also introduced more contrast into eachband. Furthermore, the relative values of the bandshave been shifted according to the spectral propertiesof ‘‘daylight’’ compensation. This has caused somered values to exceed the green values in Fig. 10.However, the KAI of the blue band has introduced asignificant amount of noise and therefore reduced itsspatial resolution. The two methods of interpolationproduced relatively similar results for the spatial

resolution of the red band and therefore it is notshown in Fig. 10.

The KAI inherently includes edge enhancement,which may not always be appropriate, for someobjects. For example, naturally smooth changes be-tween different shades of the same colour in vegeta-tion need not be enhanced, for most purposes. Itcould however be appropriate for the edges ofbranches against soil or leafy vegetation to be en-hanced. Fig. 11, which is part of an image of alogged cutting unit, illustrates one example of asituation where the edge enhancement introducesnew artefacts. The features away from the skid roadgenerally appeared to be appropriately enhanced buta sharp unnatural kink has been introduced into theleft-hand side of the road because horizontal orvertical edges appear to be enhanced in preference todiagonal ones. This could create complications forspatial image analysis, such as computer automatedobject detection. As edge enhancement is within theregion of a few pixels, the preferentially horizontaland vertical edge enhancement could be related tothe different location of edges in different bands

Ž . Ž .Fig. 10. Images and spectral profiles of forest canopy in July 1999 and after interpolation by a NNI and b KAI with daylightcompensation.


Fig. 11. The green band from a CIR image of a forestry skid roadŽ . Ž .after logging with interpolation by a NNI and b KAI. The

Ž .arrow in b points to a spurious kink in the road edge.

Ž .noted by Mason et al. 1997a . Fig. 11b also revealsthat the KAI has carried out some smoothing withina distance of about four pixels.

For the image in Fig. 11b, the compensationcorrection ‘‘none’’ was chosen in the Kodak soft-ware. When ‘‘daylight’’ compensation was chosen,the interband contrast was too great and many areasof the red and blue bands became saturated, i.e. to255 DN. Using a shorter exposure time may havereduced this saturation effect. Even with the ‘‘none’’compensation, the blue band still became saturatedafter KAI.

7. Discussion and conclusions

Our investigations revealed several practical as-pects pertinent to the interpretation of digital im-agery from the DCS460c. The more important of

these aspects are discussed here along with theirimplications for the most fruitful methods for scien-tific analysis of target dependent features in theimagery.

Ž . Ž .Both Pellikka 1998 and King 1995 used day-light to acquire parameters for vignette effect correc-tion. The latter author regarded his laboratory lightsource as unrepresentative in terms of spectrum andillumination level. Daylight varies in its spectralproperties due to varying humidity, dust, otheraerosols and the sun angle and the target can varywith seasonal effects. Consequently, the relative bandintensities of any vignette effect template will neverbe precisely the same as those on the day of aerial

Žimage collection even viewing a unique Lambertian.surface , unless a unique template is collected for

each flight. However, in our correction method, al-though a vignette correction template is used, theoverall process has a significant empirical compo-nent. The bands are treated individually, their rela-tive maximum intensities are unaltered, minima aretaken into account and when possible the imageitself can be used to create a vignette correctiontemplate. Our analysis of different flat-field imageryand a preliminary trial of smoothed forest imageryindicated that smoothed sample imagery might pro-vide at least as good a vignette correction templateas the Fostec template. To date, this is only anempirical approach and as such would require closeexamination of each sample image corrected thisway, to check for accuracy of the correction. Ourformula for correction of vignette effects should beapplicable to multispectral imagery derived from awide variety of digital cameras and a wide variety oftargets. Specifically, it’s applicable to targets that donot contain distinctly different, large features in ei-ther the image centre or its extremities, e.g. it ismore applicable to rural, aerial imagery.

Many users of CCD-based cameras adapted forcooling and used for astronomy subtract the darknoise image from their samples before further correc-

Ž .tion and analysis e.g. Newberry, 1994 . In the pre-sent work, it was observed that subtracting the darknoise images did yield more colourful and slightlysharper images. However, subtracting the dark noiseis generally regarded as only an approximation of thetotal noise under illumination conditions. Each cor-rection potentially adds more noise to the image,


consequently it was not considered appropriate forour quantitative analyses.

ŽThe severe stretching via the KAI method e.g..for the blue band in Fig. 6b may have been avoided

by using the ‘‘click’’ or ‘‘none’’ compensation, ratherthan ‘‘daylight’’. However, the ‘‘click’’ compensa-tion method relies on the user choosing a representa-tive grey area in the image, therefore it would be toosubjective and hence inappropriate for many scien-tific applications. The ‘‘none’’ compensation shouldproduce more spectral consistency for multiple im-age acquisition of similar targets. However, it stillincludes KAI and the inherent adjustments in spectraover small regions. The imagery after KAI showsgood spatial resolution and has a natural lookingcolour reproduction suitable for interpretation by thehuman eye. Consequently, it is good for qualitativeviewing of the images and for seeing relevant fea-tures worthy of further investigation. However, thepossible spectral and spatial inconsistencies causedby dynamically varying interpolation and edge-en-hancements may result in data unsuitable for someremote sensing applications where objectively con-sistent data are more important than subjective en-hancements.

The vertical leakage effects indicate that for spec-tral and spatial interpretation of vegetation-cover im-

Ž .ages and for vignette templates if unique imagesthe raw red and green DN values should be wellbelow 170, i.e. below about 67% of their maximumrange. Pixels where the raw red or green DN are

Ž .higher and their nearest neighbours should not beused for quantitative spectral work. The change incorrelation for higher DN values at first appears toimply a non-linear response in the CCD array andthereby contradict the linear response seen in a simi-

Ž .lar CCD array by Stow et al. 1996 and the linearresponse generally expected from silicon-based de-

Ž .tectors e.g. King, 1995 . However, the cameras usedŽ . Žby Stow et al. 1996 were all monochrome with

.colour filters on the lenses and so neighbouringpixels did not have greatly different DN values dueto their response to different wavelengths and there-fore did not show leakage effects. For CMOS chips,the diffusion can be mathematically modelled andthen incorporated into the correction software butapparently for full-frame imagers, such as the chip inthe DCS460c, the process is presently not suffi-

ciently interpretable so as to provide an accurateŽformula to counteract the diffusion effects Adams,

.personal communication . In general, before purchas-ing a digital camera for quantitative usage the leak-age effects should be ascertained.

ŽSpectral profiles of the green fluctuation e.g. Fig.. Ž6b and the formulae in Kodak’s patents e.g. Adams

.and Hamilton, 1997 show that the spectral proper-ties of a pixel after KAI can depend on the DNvalues of its neighbours before interpolation, if thereis no sudden edge between them. Mason et al.Ž .1997a noticed different positions for the same edgein different bands. The reason for this could be thecombined influence of the green fluctuation plus theKAI. Thus, reliable spectral measurements can prob-ably only be acquired from portions of objects thatare at least about four pixels from the object’s

Ž .boundary when using KAI . Unfortunately, objectpixel size may be constrained by image acquisitionlimitations and for spectral analyses it is best to useNNI rather than KAI. KAI with ‘‘click’’ compensa-tion would be useful for semi-quantitative colourcomparisons within a single image.

The infrared wavelengths penetrate the siliconsubstrate of the CCD more than the shorter wave-lengths. Consequently, photoelectrons from infraredphotons have a higher probability of being collectedat a neighbouring pixel’s detector gate. Additionally,each CCD pixel in CIR images can respond toinfrared photons. These two effects combine to givea slightly lower spatial resolution in each band ofCIR images than in the VIS images. A third featureof CIR images is that the singularly infrared pixelsŽ .not green or red comprise only one quarter of thetotal detectors. These three aspects together lead to apossibly poor spatial resolution for the IR band. Oneway around this may be to compare the green bandfrom a VIS image to the green band from a CIRimage and the difference should be due to the in-frared light from the target. This would have to bedone after vignette effects correction, topographiceffects, bi-directional reflectance distribution func-

Ž .tion BRDF correction, calibration and georectifica-tion.

After correction for topographically induced vari-ation in intensity and for BRDF effects the DN canbe converted to reflectance. For this conversion oneneeds to know the response of the DCS460c to


different wavelengths. Due to the broad band natureof the camera this response must be measured eitherdirectly by using a light source with an adjustablewavelength or it can be calculated by combining thequantum efficiency curves of the CCD with the lenstransmission spectra. These aspects are part of ourongoing work on processing the forest imagery.

Acknowledgements

This work was funded by the National ScienceFoundation, grant number DBI-9808312, and forT.A. Warner in part by NASA EPSCoR. The authorswould like to acknowledge the helpful interactions

Ž .with James Adams personal communication , a Re-search Physicist with Eastman Kodak and with

Ž .Wayne Shipman personal communication , a GlobalSupport Specialist with Eastman Kodak. These al-lowed us insight to some of the physics of theDCS460c. Thanks are also due to Positive Systemsfor help with interpreting the data format and forguidance with the operation of the camera. DougKing, Doru Pacurari, Stuart Phinn, Doug Stow, andCharlie Yuill, were among the university-based re-searchers in the field of remote sensing, who pro-vided useful intellectual interactions.

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