Criteria for Digital Camera Image Quality Recent Developments in … · Criteria for Digital Camera Image Quality Recent Developments in Digital Airborne Cameras 11th Annual Z/I Imaging®

Criteria for Digital Camera Image QualityRecent Developments in Digital Airborne Cameras

11th Annual Z/I Imaging® Camera ConferenceKeystone, September 21, 2007

Prof. Ralf Reulke, Dr. Andreas Eckardt

Folie 2 > Image Quality > Reulke

Motivation

New possibilities & available technology becomes important for digital camera development and applications

Digital sensors (VIS, IR, LIDAR, RADAR, hyperspectralsystems)Direct geo-referencing (determination of EO for each camera

frame or line)Direct coreferencing and alignment of high resolution stereo

channels with the multispectral channels allows combined stereo & remote sensing


Motivation

Absolute radiometry (remote sensing)Geometric & radiometric lab-calibration of the whole sensor

(as function of air pressure & temperature)

Real time processing capabilities Wireless data transmissionNew applications – Virtual glob


3D globe viewer

3D globe viewer with elevations and satellite images (Virtual Globe)Major players in the Virtual Globe arena are Google Earth, Microsoft’s Virtual Earth and NASA World WindSearch for locations through queries and user-interface controlsAdd data onto the map, like roads, political boundaries and basic image overlaysFind out information about local businesses, driving directions and other interesting thinks through this conceptThrough API’s and XML-based interfaces, developers and advanced users can create new functions and data products

(http://charlotte.utdallas.edu/mgis/ClassFiles/gisc6383/techassess_2005/VG_Report.doc)


Outline

Sensors RequirementsDetector Arrays, Resolution (Improvement), TechnologyTerrestrial, Airborne and Space Borne Imaging SystemsQuality assessment for imaging systems

New ApplicationsSensor & Data Fusion Radiometry / Classification

Conclusions


Digital Camera-Systems

Motion of the camera

Optical system(& filter)

Discretize element

Continues (analogues) Object

Discrete (digital) Image

EO determinationGPS & INS

Continues (analog) filtered image + EO

Digital photogrammetriccamera-system

Platform


Requirements (Geometry)

Number of pixel in each image directionArea to cover and object resolutionE.g. A=2 ·2 km² & Δ=20 ·20 cm² ↔ 10,000 ·10,000 pixel = 100 MPixel

Resolution = ground sampling distance (GSD)Sampling theorem, well suited optics

Optical system requirement from Pixel distance (PD)Optics resolution [lp/mm] = 1000 / (2 · Pixel Distance [µm])E.g. Δ=10µm → 50 lp/mm

Image smear (optics, pixel size, airborne platform movement) canbe described by Gaussian σ≈0.5..1 PD

Airplane speed ≈ 70 m/s, GSD=10cm → 1ms integration time= 0.7·Δsmear


System Parameters

Field of View (Degree)Swath width (km)Instantaneous FOV (micro radian) Number of spectral bands and spectral rangesQuantization (Dynamic Range)

The Dynamic Range of the Detector is defined by the ratio of saturation output to RMS noise in the dark. The Dynamic Range for all pixels is in the range of 12-14 Bit

MTF (Modulation Transfer Function) curves Weighted MTF for the Sensor at Nyquist frequency and at a fixed readout frequency


Optics Parameter

Effective Focal length (mm) Aperture - F number Wavelength range (Nanometer) Aberration (spherical, coma, field curvature, astigmatism, distortion, lateral color) PSF / MTF


Focal plane / detector subsystem

Detector types, number of pixelsEffective Pixel distance (pitch) and pixel size (micron) Spectral sensitivity, spectral bandsCharge saturation (electron) quantum efficiency (electron/photon), Scale factor, responsivityPixel rate and line rate Shutter options and readout timing. Focal plane dimensions Temperature of Operation


Focal plane / detector subsystem

Quantization levels and dynamic range (number of effective bits)LinearityBlooming (%) The sensor system shall have an anti blooming provision in across track directionSmear (%)


Sensor Design

Detector arrayCCD-Matrix, CCD-line

OpticsAssembling

Number of focal planeHow to fill the gaps


High Resolution Detector-Arrays, CCD-Matrices

High resolution matrices (> 100 MPixel) are availablehttp://www.dalsa.com/news/news.asp?itemID=252Commercial high resolution photo systems in a price range up to 10 T€

have > 20 Mega-Pixel Matrices≈100 Mpixel matrices seem to expansive for standard applications

KAF-31600 6496 x 48726.8µm square pixel

31.6Mpixel Kodak

KAF-39000 7216 x 5412 39Mpixel Kodak

FTF5066 5kx6.6k7.2µm square pixel

33Mpixel DALSA

???? 10.56x10.56k 111Mpixel DALSA

CCD 595 9kx9k 81Mpixel Fairchild Imaging


Digitale Backs

eyelike JenoptikHasselblad (former: Imacon) (http://www.hasselblad.com/)Leaf

Leaf Aptus 75, 33 Mio. Pixel (Dalsa?)

PhaseOne (http://www.phaseone.com/)PHASE ONE’S 39 Mio. Pixel (Kodak?)

Sinar (http://www.sinar.ch/)Sinarback eMotion75 with 33 million pixels

Digital backs for airborne systems


New Sensor TechnologiesThe Feature: Sensors with very high frame rate

Fairchild Imaging has produced 9k x 9k sensors for many years 2 f/s

The Challenge: 8k x 8k @ 1000 f/sMost challenging requirement is 67 Gigapixel data rateFairchild Imaging 1000 f/ssensors produced for many years

Fairchild Imaging CCD 5959k x 9k CCD


New Sensor TechnologiesHybrid Low Light Level Applications

Xinqiao (Chiao) Liu, Boyd A. Fowler, Steve K. Onishi, Paul Vu, David D. Wen, Hung Do, and Stuart Horna, CCD / CMOS Hybrid FPA for Low Light Level Imaging, Fairchild Imaging, Inc., & U.S. Army Night Vision and Electronic Sensors DirectorateCombines CCD imaging characteristics (e.g. high quantum efficiency, low dark current, excellent uniformity, and low pixel cross talk) with High speed, low power and ultra-low read noise of CMOS readout technology

http://www.fairchildimaging.com/main/documents/CCD_CMOS_Hybrid_FPA_for_Low_Light_Level_Imaging.pdf


New Sensor TechnologiesHybrid Low Light Level Applications

MicroscopyLive cell fluorescence Fixed cellConfocal

X-ray ImagingRadiographyFluoroscopyX-ray crystallography

Astronomy & Space ResearchAdaptive optic wave front sensorStartrackersEnvironmental sensing

Night VisionNear Term -- aircraft, vehicle, fire controlMedium Term -- manportable

QE[400nm]:>75%


New Sensor TechnologiesHigh Resolution RGB - Sensors

In difference to film typical RGB-sensors has a filter raster on the chip

Foveon X3 image sensors have three layers of pixel sensorsSigma SD9 (2268 x 1512 pixel)

http://www.foveon.com/X3_tech.html


dIGIcam-K14 Geometrische Auflösung

red green blueColour

31cm 22cm 28cm

ParameterHight a.g. 550m

Pixel size 8μmFocal legth 28mmScale 19000GSD (theor.) 16cmBayer pattern

Colour interpolation Undersampling: full detail/colour not attainedBlur filters to reduce aliasing artifacts

Bayer pattern decomposition


High Resolution Detector-Arrays, CMOS-Detector

Lower power usageIntegration of additional circuit on-chipLower system costDirect pixel accessNon-linear response characteristics


(Geometric) Resolution Improvement

Subsempling techniques, only for still imagingJenScan- Camera (Kontron-Progress-Camera) Heimann

Biometric SystemsTypical CCD-matrix with much smaller pixel size, moving in a

sub-pixel rangeFrom 3x3 up to 6x6 sub-sampling stepsProblems: SNR & MTF / moving objects

Using more than one arrayProblem: Filling the gaps


Resolution Improvement

The JVC Camcorder GC-QX3U uses AIS (Accurate Image Shift). Lens features high quality optics including two aspherical lensesImage-shift technology that doubles the image dataUXGA (1600 x 1200) 1.92 Megapixel Digital Stills with Pixel Shift

Technology

See staggered array – SPOT 5


High Resolution Detector-Arrays, CCD-Lines

Manufacturer Model Photopixel Size [µm²]

Atmel TH7834 12000 6.5×6.5Atmel customise 2×12000 6.5×6.5EEV CCD21-40 12288 8×8

KODAK KLI-10203 3×10200 7×7

KODAK KLI-14403 3×14204 5×5

Fairchild Imaging CCD194 12000 10×8.5

SONY ILX734K 3×10500 8×8


EyeScan – a High Resolution Panoramic Camera (KST & DLR)

Number of pixel 3∗10200 (RGB)

Radiometric dynamic / resolution

14 bit / 8 bit per channel

Shutterspeed 4 ms ... 512 ms

Datarate 2.5 Msamples/s/channel15 Mbyte/s

Datavolume for 360°scan

3 Gbyte

Acquisition time 4 min

Power supply 12 V


Auckland, NZ

High resolution panoramic camera -Example


Low Light Panoramic Camera (DLR)

Designed for basic research of TDI systems:• Synchronization issues• Geometric calibration of TDI sensors• DSNU & PRNU issue in dependency to the temperature• MTF measurement• SNR measurements


Digital Photogrammetric Airborne Camera Systems (Matrix based)

Commercial SystemsDMC (Intergraph)UltraCamD Vexcel Imaging AustriaADS40 (Leica Geosystems)

HRSCother


http://www.intergraph.com/dmc/default.asp

4 high-resolution 7k x 4k PAN camera heads 4 multispectral 3k x 2k camera

heads Camera electronic unit 3 FDS units, each with 576 GB disk

space for the storage of 4,400 images total

Digital Mapping Camera System (DMC)


Pre-processing of DMC-image data

DMC pre-processingrelative orientation of the 4 camera heads is knowngeometric & radiometric correction (mosaicking)Image with (quasi-) central perspectiveFusion with colour channels

© Z/I-Imaging, 2001


DMC, example imagesLeft: (digitized) film - Right: digital camera

Vaihingen, test flight, mai 2003

©Z/I ©Z/I


Other SystemsROLLEI

AIC modular LS, AIC integralCCD-chip with

4080 x 4076 pixels (16 Mpixel)4080 x 5440 pixels (22 MPixel)

RS232 connection for remote controlIEEE1394 data interfaceBurstrates up to 2.5 sec. / frm.



Geometric resolution targets(Siemens star, bar pattern)



red green blueColour

31cm 22cm 28cm

ParameterHight a.g. 550m

Pixel size 8μmFocal legth 28mmScale 19000GSD (theor.) 16cm

Bayer patternColour interpolation Undersampling: full detail/colour not attainedBlur filters to reduce aliasing artifacts

Bayer pattern decompositionResolution Pan 6.6cm

ADS40 high-end sensorHeight a.g. 500mPixel size 6.5μmFocal length 62.77mmscale 8000GSD (theo.) 5.5cm


Airborne-Camera Alpa (CH)

Airborne-camera with two lenses and two digital 33-MP-BacksACN =

AirCamNetwork

http://www.alpa.ch/files/knowledgebase/18/ALPA_ACN.pdf


CCD-line Stereo Cameras

Airborne cameras 3 line Principle for along track camerasDerenyi, University of New Brunswick, Canada 1970Hofmann about 1985

Space borne cameras


WAOSS (Wide Angle Optoelectronic Stereo Scanner)WAAC (Wide Angle Airborne Scanner)HRSC (High Resolution Stereo Camera)ADC / ADS40 (Airborne Digital Camera / Sensor)MEOSS (Monocular Electro-Optical Stereo Scanner)MOMS-02 (Modular Optoelectronic Multispectral Stereo Scanner )DPA (Digital Photogrammetric Assembly)TLC

Airborne CCD-Line Scanner


ADC-EM2, Reichstag, 23.4.99, h=3 km, Δ≈25cm


Digital Photogrammetric Airborne Camera Systems

High Resolution Frame Cameras with ≥100 Mpixel are availibleMinimum GSD < 10 cmRadiometric dynamic ≈ 12 bitDirect georeferencing is possible


High-resolution Imaging Sensors on Satellite Platforms

High-resolution mapping means a pixel distance between 0.5 m to 3 m.

Spot0.5 to 2 m systems Ikonos and Eros, QuickBird, OrbView 3Governments (France, Japan, China-Brazil, India) have

announced and launch high-resolution systems

http://www.ipi.uni-hannover.de/html/publikationen/2006/paper/KJ_Damaskus.pdf


High-resolution Imaging Sensors on Satellite Platforms (IKONOS)

Digital camera systems was designed and built by Eastman Kodak Company, Rochester, NY., Collect panchromatic (gray-scale) imagery with one-meter

resolution, and multispectral data (red, green, blue, and near infrared) with four-meter resolution First image 1999 (Washington memorial)

Altitude 681 kilometers

Inclination 98.1 degrees

Speed 7 km/s

Orbit time 98 minutes

Orbit type Sun-synchronous


IKONOS - Space Imaging: Rom Petersplatz


Quick Bird - Digital Globe: Rom Petersplatz


Nr. of Pixels PAN 24.000 PAN-Sensor 2 x 12.080-TDI Line Rate PAN 10 kHz +5/-50 % CCD Output Rate 16 x 15MPixel/s Data Rate 3,84 Gbit/s MS-Sensor 8 x 6.000-TDI Line Rate MS 2,5 kHz +5/-50 % CCD Outp. Rate/Colour 2 x 7,5 MPixel/s Data Rate 4 x 240 Mbit/s Pitch PAN 8,75 µm Pitch MS 2 x 17,5 µm Anti Blooming yes Operating temperature 10°-25°C Image Plane dx 22 cm Dynamic Range 14 Bit PRNU yes DSNU yes SNR-PAN >200 SNR MS >200 Orbit 685 km Focal Length 8,6 m F-# 12 PAN 450 nm-900 nm NIR 760 nm-900 nm RED 630 nm-690 nm GREEN 520 nm-600 nm BLUE 450 nm-520 nm

CEU Development KompSat 3 [EADS Astrium GmbH & DLR]DLR is responsible forThe Focal Plane and FEE Development


Determination of Sensor Parameter – Spatialand Radiometric Resolution

Measurement principlesPSF / MTF, spatial resolutionSignal calculation, radiometric resolution


Image Quality – Spatial Image ResolutionHow to Determine Resolution Data?

Resolution charts

Film MTF(KODAK AEROCOLOR III Negative Film 2444)

x

y

v

x‘

y‘

uPoint source in the object space

Gray level distribution in the image space

Imaging

PSF & MTF evaluation


α= 1,22 λ / D (in rad) dPixel = 1,22 λ F# (in m)

Spatial Resolution, Rayleigh – Criterion Diffraction Limit of a Telescope


Image Quality – Radiometric Image Resolution

Dynamic range > 12 bit (4096 gray levels)Sensor electronics noise 0.1 ... 0.5 % from dynamic rangeSignal calculation

E = π/4 f# · cos4θ ∫dλ Tλoptics · Tλ

up · Lλtarget

Signal depends from(spectral) illuminationAthmosphere & cloudsSpectral transmission of the otical system (lenses & filter)AperturePixelsize / -areaResponsivity of the detector arrayIntegration time

Only integration time and aperture (?) are variableSpectral limitations (e.g.for RGB) increase radiometric

problems

[ ]DV V el DNs A E R E DNhcλ

=η ⋅η ⋅η ⋅τ⋅ ⋅ ⋅ = τ⋅ ⋅


2 2 2_

e

e CCD rms channel ADC

nSNRn σ σ σ

=+ + +

SNR Model

ne : Photon NoiseσCCD_rms : CCD rms Noiseσchannel : Electronics channelσADC : ADC


dx µm 1 2 n

1 2 n

t1[µs] t2[µs] tn[µs]

GSD

Optik

dx µm 1 2 n

1 2 n

t1[µs] t2[µs] tn[µs]

GSD

OptikTDI Principe

Electronically increasing of the aperture

StepsTDINrSNRPhotonSNRPhoton TDI ____ ∗=

Advantage:


Radiometric Resolution

Reichstag, Berlin

Schattenbereich

Sonnenbereich


System Quality Criteria

Depends from applicationStandards?

German standard DIN 18740-4 “Anforderungen an digitale Luftbildkameras und an digitale Luftbilder”NATO STANAG 3769 (Minimum Resolved Object Sizes and Scales for Imagery Interpretation) http://cartome.org/min-rez.htmNational Image Interpretability Rating Scales (NIIRS) (http://www.fas.org/irp/imint/niirs.htm)General Image-Quality Equation: GIQE (http://adsabs.harvard.edu/abs/1997ApOpt..36.8322L)


NATO STANAG 3769 (Minimum Resolved Object Sizes and Scales for Imagery Interpretation)

DefinitionsDetection: In imagery interpretation, the discovering of the existence of an object but without recognition of the object. Recognition: The ability to fix the identity of a feature or object on imagery within a group type, ie, tank, aircraft. Identification: The ability to place the identity of a feature or object on imagery as a precise type. Technical Analysis: The ability to describe precisely a feature,object or component imaged on film.


Spatial Resolution


NATO STANAG 3769 (Minimum Resolved Object Sizes and Scales for Imagery Interpretation)

Object

Detection

Recognition

I Identification

Technical Analysis

Terrain

~ 800 m

90 m

3 m

0.75 m

Urban Areas

60 m

15 m

3 m

0.75 m

Urban Areas

30 m

6 m

1.5 m

0.4 m

Surface Ships

15 m

4.5 m

0.15 m

0.04 m

Coast and Landing Beaches

15 m

4.5 m

1.5 m

0.4 m

Bridges

6 m

4.5 m

1.5 m

0.3 m

Airfield Facilities

6 m

4.5 m

3 m

0.15 m

Aircraft

4.5 m

1.5 m

0.15 m

0.04 m

Vehicles

1.5 m

0.5 m

0.15 m

0.04 m


National Image Interpretability Rating Scales (NIIRS) (http://www.fas.org/irp/imint/niirs.htm)

The aerial imaging community utilizes the National Imagery Interpretability Rating Scale (NIIRS) to define and measure the quality of images and performance of imaging systemsThrough a process referred to as "rating" an image, the NIIRS isused by imagery analysts to assign a number which indicates the interpretability of a given imageThe NIIRS concept provides a means to directly relate the quality of an image to the interpretation tasks for which it may be usedNIIRS provides a systematic approach to measuring the

quality of photographic or digital imagery, the performance of image capture devices, and the effects of image processing algorithms


General Image-Quality Equation: GIQE

A regression-based model relating aerial image quality, expressed in terms of NIIRS, to fundamental image attributesGIQE treats three main attributes:

scale, expressed as the ground-sampled distance; sharpness, measured from the system modulation transfer function; and the signal-to-noise ratio

The GIQE can be applied to any visible sensor and predicts NIIRS ratings with a standard error of 0.3 NIIRSThe image attributes treated by the GIQE are influenced by system design and operation parametersThe GIQE allows system designers and operators to perform trade-offs for the optimization of image qualityJon C. Leachtenauer, William Malila, John Irvine, Linda Colburn, and Nanette Salvaggio, General Image-Quality Equation: GIQE, Applied Optics, Vol. 36, Issue 32, pp. 8322-8328


Fusion

Sensor fusion with complementary and competitive SensorsLimitations from measurement principles (e.g. with CCD-camera have a limited spectral range)

Different types of cameras: Day & night vision: VIS, RGB – TIRDifferent types of measurement principles (Camera & laser scanner

Competitive SensorsMore than one camera of the same type observe the same area (camera node) – improvement of accuracy

References:L. Klein; Sensor & DataFusion, SPIE presshttp://www2.informatik.hu-berlin.de/cv/ (Anforderungen an geometrische Fusionsverfahren, Workshop, 20. November 2006)


Fusion

„Data Fusion is a formal frame work in which are expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of ‚greater quality‘ will depend upon the application.“

Wald, L. (1999): Some terms of reference in data fusion. IEEE Transactions on Geosciences and Remote Sensing, 37(3), pp. 1190-1193


Fusion

Fusion of high resolution image data withData from different spectral channelsLaser scannerRadarHyperspectral systems

http://www2.informatik.hu-berlin.de/cv/index.php?seite=3&sub=4&site=./conferences/Fusion_Workshop_11.2006/index.html


Airborne Laserscanner

http://www.photogrammetry.ethz.ch/summerschool/pdf/08_Brenner_aerial_scanner.pdfAirborne laser scanning principles

LaserSemiconductor lasers or Nd:YAG lasers pumped by semiconductor lasers, emits at λ = 1064 nm (near infrared)810 nm (ScaLARS), 900 nm (FLI-MAP), 1540 nm (TopoSys)

Pulsed laser ↔ Continuous laser operation


Resolution of LIDAR data

„Typical“ LIDAR dataRow data 1-2m point distanceDSM 1m RasterDEM 10m, 5 m, 1m Raster

Image data25 cm GSD

Artifacts in the ortho image due to weak LIDAR data resolution


The Mount Etna Case Study: A Multisensor View

German Aerospace Research Establishment (DLR), Germany R. Horn, K. P. Papathanassiou, A. Reigber, R. Scheiber (Institutfür Hochfrequenztechnik)P. Hausknecht, P. Strobl, R. Boehl (Institut für Optoelektronik)M. Scheele, R. Reulke, W. Baerwald (Institut fürWeltraumsensorik)

G. Puglisi, M. Coltelli (Istituto Internazionale di Vulcanologia),G. Fornaro (Istituto di Ricerca per L’Elettromagnetismo e i Componenti Elettronici),Consiglio Nazionale delle Ricerche (CNR), Italy


The experiment objectives

The generation of a high precision DEM using airborne microwave and optical sensorsThe retrieval of surface parameters using radar polarimetry and optical spectrometryThe investigation of the synergy potential of optical and microwave sensorsThe exploration of the limits of airborne differential SAR interferometry


Results


Results


Results


Results


Results

Geometry - Layover, foreshortening, shadowing. Critical issues for radar.GPS - Positioning accuracy depends on GPS signal quality. This is crucial for geocoding to be performed as required.Weather - Clouds influence the optical part of multisensorsystems, radar is more flexible.Synergy - The information delivered by optical and microwave sensors is complementary.


Radiometrie / Classification

New business with derived products

RapidEye: http://www.rapideye.deRapidEye is a commercially funded provider of geospatial information and customized solutionsOffer geospatial management information customized to the needs of clients in the Agriculture, Forestry, Oil & Gas, Environmental and Governmental marketsBased on five-band multispectral image data sets acquired by RapidEye satellites. Products can be radiometrically and geometrically correctedDerived data are vegetation index, image classification, etc.


Atmospheric Correction

Radiation components for the @sensor radianceReflected radiation (LReflected) from a certain pixelPath radiance (LPath) - photons scattered into the sensor’s

instantaneous field-of-view, without having ground contact

τ, ρ and Eg are the ground-to-sensor atmospheric transmittance, surface reflectance, and irradiance on the groundOr for the surface reflectance

DNccE

LLLL gPathflectedPath ⋅+=

⋅⋅+=+= 10Re πρ

τ

( )g

Path

ELDNcc

⋅−⋅+

⋅=τ

πρ 10


Lab-Measurement

Concept based on Ulbricht-sphere → L (radiance)Absolute accuracy of the signal of

the Ulbricht-sphere is about 2 %Integration time: 7 msAperture

56 -> 5.680 -> 8.0110 -> 11.0160 -> 16.0

TDI – 0, 2, 5, 10


ResultsAtmospheric model: urbanVisibility is unknownFlight 01, visibility = 25Flight 01, visibility = 25Calculate calibration with ATCOR4 (Richter, DLR)


Pixel-based classificationBased on spectral information of pixels

Object- oriented classificationBased on spectral & spatial information of pixels

Unsupervised classification-ISODATA- Kmeans

ImageClassification

1. Defining number of classes2. Defining training samples3. Examining validity of training samples4. Selection of classification algorithm

Minimum user input

Post classificationAccuracy Assessment

1. Multi resolution segmentation2. Knowledge-based classification of segments

Supervised classification- Parallelepiped-Minimum distance-Mahalanobis distance-Maximum likelihood

Image Classification


Maximum Likelihood

Supervised Classification Outputs derived from DMC-DataParallelepipedOriginal image


Conclusions

New developments in high-resolution imagingTechnology opens new opportunities, different camera geometry

and (absolute) multispectral capabilitiesImage quality and quality of derived products are important for

the customersDigital systems make new applications possible (and necessary),

because of a new kind of information, new products and data fusion with products from other data sourcesNew products are possible from radiometric calibrated data

Radiometric calibration in the lab is a challenging taskDigital camera systems have the potential to substitute film

cameras and have the ability to generate new products

Documents

Criteria for Digital Camera Image Quality Recent Developments in … · Criteria for Digital Camera Image Quality Recent Developments in Digital Airborne Cameras 11th Annual Z/I Imaging®