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Projection-Based Image Registration in the Presence of Fixed-Pattern Noise

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Projection-Based Image Registration in the Presence of Fixed-Pattern Noise Stephen C.Cain,Majeed M.Hayat,Senior Member,IEEE,and Ernest E.ArmstrongAbstract—A computationally efficient method for image regis-tration is investigated that can achieve an improved performance over the traditional two-dimensional(2-D)cross-correlation-based techniques in the presence of both fixed-pattern and temporal noise.The method relies on transforming each image in the sequence of frames into two vector projections formed by accu-mulating pixel values along the rows and columns of the image. The vector projections corresponding to successive frames are in turn used to estimate the individual horizontal and vertical components of the shift by means of a one-dimensional(1-D) cross-correlation-based estimator.While gradient-based shift esti-mation techniques are computationally efficient,they often exhibit degraded performance under noisy conditions in comparison to cross-correlators due to the fact that the gradient operation amplifies noise.The projection-based estimator,on the other hand, significantly reduces the computational complexity associated with the2-D operations involved in traditional correlation-based shift estimators while improving the performance in the presence of temporal and spatial noise.To show the noise rejection capability of the projection-based shift estimator relative to the2-D cross correlator,a figure-of-merit is developed and computed reflecting the signal-to-noise ratio(SNR)associated with each estimator.The two methods are also compared by means of computer simulation and tests using real image sequences.Index Terms—Fixed-pattern noise suppression,image registra-tion,motion estimation,vector projection.I.I NTRODUCTIONI MAGE registration is commonly used to provide electronicstabilization of sensors or to facilitate the processing of multiple motion-compensated frames in order to improve image quality(e.g.,by means of temporal averaging).Some new appli-cations of registration also include resolution enhancement from multiple frames of data[1]–[5]and nonuniformity correction in focal-plane arrays[1],[6]–[8].In many situations,the perfor-mance of algorithms that employ image registration information relies heavily on the accuracy of the shift estimates.The accuracy of the shift estimates generated using various registration algo-rithms is,in turn,strongly affected by the temporal and spatial Manuscript received July7,2000;revised August30,2001.This work was supported by the National Science Foundation(CAREER Program MIP-9733308).The associate editor coordinating the review of this manuscript and approving it for publication was Dr.Christoph Stiller.S.C.Cain is with the Department of Electrical and Computer Engineering, University of Dayton,Dayton,OH45469-0245USA and also with the De-partment of Engineering,Purdue University at Fort Wayne,Fort Wayne,IN 46805-1499USA.M.M.Hayat is with the Electro-Optics Program and the Electrical and Computer Engineering Department,University of Dayton,Dayton,OH 45469-0245USA and also with the Electrical and Computer Engineering Department,The University of New Mexico,Albuquerque,NM87131-1356 USA(e-mail:hayat@;mhayat@).E.E.Armstrong is with OptiMetrics,Inc.,AFRL/SNJM,Wright Patterson AFB,OH45433-7700USA(e-mail:ernest.armstrong@). Publisher Item Identifier S1057-7149(01)10571-3.noise inherent in all sensors[9].Temporal noise,which fluctuates randomly from frame to frame,includes photo-detection noise, background or stray light,dark current,read-out noise,etc.In contrast to temporal noise,spatial noise has the characteristic that its realizations do not vary from frame to frame resulting in an ambient spatial pattern which superimposes itself on the true scene[10].This spatial noise is often referred to as fixed-pattern noiseand is duetothe factthateverydetector inthe arrayresponds differently to light.Fixed-pattern noise is especially undesirable in image registration since any pattern that remains stationary in time will tend to bias any shift-estimation algorithm toward solutions that favor no shifts between the frames of image data. Fixed-pattern noise can be reduced by calibrating the sensor by means of imaging target scenes with uniform intensities. Fixed-pattern noise can also be reduced from sequences of video by post-processing algorithms[6],[7],[11].However,some of these methods inherently rely on accurate image registration of the raw video[8].It is therefore very desirable to have a registration algorithm that is tolerant to fixed-pattern noise.A commonly used registration technique is based on max-imizing the two-dimensional(2-D)cross-correlation function between successive frames,in which case,the shift estimate is defined as the maximizing point[12].The2-D cross-corre-lator is generally regarded as a robust estimator in the presence of noise in comparison to more computationally-efficient es-timators.Unfortunately,its implementation involves three dis-crete-Fourier transforms(DFTs)at the size of the image.Gra-dient-based shift estimation techniques[2],[13],on the other hand,have a reduced computational complexity but are sensi-tive to fixed-pattern noise since they may measure false spatial gradients that will be consistently present in successive frames. In this paper,the concept of integral projection is employed to develop a projection-based registration technique that is com-putationally efficient and can achieve improved performance over the traditional2-D cross-correlation-based techniques in the presence of both fixed-pattern and temporal noise.This concept was first conceived from Huang’s observation that the linear phase associated with simple translations is encoded on the Fourier axes[14].Since then the integral projection shift-estimation technique had been introduced to help accom-plish inter-frame image encoding by registering subblocks within an image with subblocks within successive frames in a video sequence[15]–[17].Under the method considered in this paper,each image in a sequence is transformed into two vector projections,formed by accumulating pixel values along the rows and columns of the image,which are subsequently used to estimate the horizontal and vertical components of the shift by means of a one-dimensional(1-D)cross-correlation-based1057–7149/00$10.00©2001IEEEestimator.All of the 2-D DFTs associated with the traditional cross-correlator are therefore replaced by 1-D operations.Most importantly,by virtue of the row/column averaging mech-anism involved in the projections and the mutual statistical independence of the response characteristics of the detectors,the effect of fixed-pattern noise can be significantly reduced in many practical situations.Although this 1-D technique for image registration has been used in other similar applications [15]–[17],the notion that the projection operation improves performance over the 2-D correlator in the presence of either temporal or fixed-pattern noise has been either completely unknown or not well understood.It is the goal of this paper to reveal the mechanism by which this improved performance is realized and provide a mathematical framework by which the performance improvement can be understood and quantified.This paper is organized as follows.In Section II,a description of the projection-based estimator is given and an explanation is presented as to why the projections preserve the shift informa-tion.In Section III,a signal-to-noise ratio (SNR)analysis is per-formed that compares the performance of the 2-D cross-correla-tion shift estimator to that of the projection-based shift estimator in the presence of both temporal and fixed-pattern noise.A per-formance figure-of-merit is defined for both shift estimators and evaluated using two image models that reflect scenarios when the image possesses high and low degrees of statistical spatial correlation.In Section IV ,computer simulations are generated demonstrating the performance of the projection technique on simulated image sequences.In Section V ,real image sequences are used to demonstrate the performance of the projection tech-nique.The main conclusions are summarized in Section VI.II.T HE P ROJECTION -B ASED S HIFT E STIMATORConsider observing two successive measurements (frames)of ascenematrix whose elementsare(1)whereis translated by anamountandandandwhere the shifts are assumed for convenience to be circular.(Border effects resulting from the circular shift become negli-gible as ,by theruleand(3)andandandcan be writtenasrespectively,where .We now substitute the above projections into the expression for theprojection-based shift estimator given in(3)and apply the con-volution property of the DFT toobtain,which is definedby,which impliesthat.In any real imaging system,the scene does not shift circularly,which causes new information to enter the field of view.Thisnew information is similar to noise,which makes the Fourierarguments for accurate image registration less valid.The pro-jection-based estimator is sensitive to new information enteringthe scene and so the following noncircular correlation estimateis developed with the projections as opposed to a circular cor-relation estimate.In order to help the projection-based shift es-timation algorithm ignore new information entering the scene,we introduce the windowfunction that has the followingspatialstructure:ifandotherwise.In theinequality,,whereis the average value of this projection.Sim-ilarly,the projection in the vertical direction is formed in thefollowing manner.The shift estimate is determined from(3)byan exhaustive search over allintegers,we define the SNR atanyshift as the ratio between the effective signal squaredand the sum of the variance of the noiseat and the vari-ance of the noiseat,to de-fine thefigure-of-merit(5)where the factors appearing in the denominator are condi-tional variances(e.g.,)and the expectation in the denominator of(5)represents ensemble averaging of the image.If noise werenot present in thesystem,correlation is not always dependent on these differences,the figure-of-merit is a functionover(6)and(8)andis assumed to be temporally fixed between the two observedframes and its entries are assumed to be independent andidentically distributed random variables each with a varianceofandand,appearing as the auto-correla-tionfunction,An example of imagesthat possess this property include astronomical images ofunresolvable stars.With the above auto-correlation model,theexpressionforthatminimizeswhich can be maximizedover to yield the upperbound).In this special case,thefigures-of-meritwithin and obtain a versionof only.This assumption does notaffect the computationoffrom which it followsthatin the expressionforthat minimizes theratio,theratioandFig.2.Images showing a star in the presence of temporal noise at two different times.shift estimator will have a higher SNR than the 2-D cross-cor-relation shift estimatorwhenever.IV .S IMULATION R ESULTSIn this section,a set of simulations is generated to demon-strate the validity of the conclusions established in the previoussection.The first set of simulations feature an image of a simu-lated star field.This image exhibits no spatial correlation (ap-proximating the uncorrelated image model considered in the previous section)and is corrupted with temporal Poissonian noise.(We use the Poisson model as a convenient approximation that captures the signal-dependent nature of temporal noise es-pecially in cases when shot noise dominates the sensor read-out noise.)These images are shown in Fig.2.The operation of generating two circularly shifted versions of the scene is re-peated 100times and shift estimates are generated using both the projection-based and 2-D correlation methods.These shift estimation algorithms are implemented exactly as described in Section II with a search range of seven pixels.The registra-tionparametersare integers as are the shifts gener-ated in the simulation.The cross-correlations are computed and the maximum is chosen over the entire range of shifts for both algorithms.Although real sequences of data generally possess noninteger and noncircular shifts,these assumptions are ade-quate for comparing the performance of the shift estimation techniques.The shift estimates are compared with the known synthetic shifts for accuracy.The shift estimation error in pixels is com-puted by squaring the difference between the known synthetic shifts and the estimated shifts and summing this squared error over all frames.The result of this summation is then divided by the number of frames used in the test and the square root of this result is taken as the average error with units of pixels.The 100trials are used to establish the statistical behavior of the shift estimation techniques.The results are shown in Table I.The performance of the two shift estimators are shown as a func-tion of the SNR.The SNR is defined as the amplitude of the signal divided by the standard deviation of the noise.The peak signal-to-noise ratio (PSNR)in an image is then the highest SNR achieved within the frame.These results verify that in-deed the 2-D cross-correlation shift estimator outperforms the projection-based shift estimator when the scene is spatially un-correlated.Next,a fixed-pattern noise is added to each frame and the standard deviation of the fixed-patternnoiseFig.3.Example of star images with fixed-pattern noise added to them.PSNR of the data is fixed at ten and the ratio of fixed-pattern noise variance to temporal noise variance is varied.These re-sults again verify that the lack of correlation in the scene causes the2-D cross-correlation shift estimator to outperform the pro-jection-based shift estimator.In general,we have observed that the performance of the two estimators becomes comparable as the fixed-pattern noise variance increases.The next set of data is generated in the same manner as in the previous simulations using the scene shown in Fig.4,which possesses strong spa-tial correlation.This scene was chosen because its auto-correla-tion is similar to the linear auto-correlation model used in Sec-tion III.The auto-correlation function of the scene is shown in Fig.5.The results,shown in Table III demonstrate that the pro-jection-based shift estimator outperforms the2-D cross-corre-lation shift estimator.Finally,Table IV shows the performance of the two estimators in the presence of fixed-pattern noise.The PSNR is fixed at26when the fixed-pattern noise is small with respect to the temporal noise.The fixed-pattern noise variance is adjusted to be a fraction of the temporal noise variance to produce the various ratios of fixed-pattern to temporal noise variance shown in Table IV.These results show that the perfor-mance improvement achieved using the projection-based shift estimator is more significant when fixed-pattern noise is a se-riousconcern.Fig.4.Cloud scene used to generate the data used to analyze the behavior of the shift estimators in the presence of high spatial correlation.To demonstrate the correlation between the simulations and the analytic expressions for the figures-of-merit,the magnitudesofandFig.5.Auto-correlation function of the scene used to test the performance of shift estimation in the presence of high spatial correlation.TABLE IIP ERFORMANCE OF THE P ROJECTION -B ASED E STIMATOR (1-D)V ERSUS THE 2-D C ROSS -C ORRELATION E STIMATOR (2-D)IN THE P RESENCE OF B OTH F IXED -P ATTERN AND T EMPORAL N OISE .N OTE T HAT THE I MAGE P OSSESSESN O S PATIAL C ORRELATION IN T HIS EXAMPLEandm wavelengths).TABLE IIIP ERFORMANCE OF THE P ROJECTION -B ASED E STIMATOR (1-D)V ERSUS THE 2-D C ROSS -C ORRELATION E STIMATOR (2-D)W HEN S PATIAL C ORRELATION I SP RESENT IN THE IMAGETABLE IVP ERFORMANCE OF THE P ROJECTION -B ASED E STIMATOR (1-D)AND THE 2-D C ROSS -C ORRELATION E STIMATOR (2-D)A GAINSTT EMPORAL AND F IXED -P ATTERN N OISE W HEN S PATIAL C ORRELATION I SP RESENT IN THE IMAGEFig.6.Plot showing the value of the figures-of-meritF and F=100; =10; =0;w = =0;and z =1.Two sample frames from this 80-frame sequence are shown in Fig.7.The 2-D cross-correlator is used to measure the shifts between frames of the sequence in integer amounts.This set of shifts is used as the true shifts for purposes of measuring the registration error when noise is added to theFig.7.Two sample frames from a sequence of infrared video data(top).Frame one of the sequence at a PSNR of2(bottom left)and PSNR of20(bottom right).video sequence.In the first test,fixed-pattern noise is added to the sequence in varying amounts to produce19sequences each possessing a different PSNR between2and20.Fig.7 shows frame one of the sequence at a SNR of2and20. Fig.8shows the error performance of the projection-based technique(dotted line)and the2-D cross-correlator(solid line) plotted as a function of the PSNR in the data.The error metric used in this study is the root mean-squared error in units of pixels.Finally,Fig.9shows the performance of the algorithms as a function of the PSNR when the noise is temporal in nature(changes from frame to frame in the sequence).In both cases it is clear that the projection-based technique produces consistently lower registration root mean-squared error. The second sequence of video is taken with a visible camera of the planet Jupiter through severe atmospheric turbulence.This type of turbulence changes the impulse response of the imaging system between observations,which will cause the image to change in its appearance from frame to frame.Two sample frames from this40-frame sequence are shown in Fig.10.The2-D cross-correlator is again used to measure the shifts between frames of the sequence in integer amounts.In the first test,temporal noise is added to the sequence in varying amounts to produce ten video sequences each possessing a different PSNR between1and10.Fig.10shows frame one of the sequence at a SNR of1and10.Fig.11shows the error performance of the projection-based technique(dotted line) and the2-D cross-correlator(solid line)plotted as a function of the peak temporal SNR in the data.Finally,Fig.12shows the performance of the algorithms as a function of the PSNR when fixed-pattern noise is added to the data.In both cases it isFig.8.Root mean-squared registration error performance of theprojection-based technique (dotted line)and the 2-D cross-correlator (solid line)as a function of peak fixed-pattern SNR for the infrared data sequence.This demonstrates the improved noise-rejection capability of the projection-basedtechnique.Fig.9.Root mean-squared registration error performance of the projection-based technique (dotted line)and the 2-D cross-correlator (solid line)as a function of peak temporal SNR for the infrared data sequence.clear that the projection-based technique produces consistently lower root mean-squared registration error.VI.C ONCLUSIONImage registration of adjacent frames in a video sequence is often desired in order to take advantage of temporal correlation in images.This process can be very computationally intensive relative to the scale dictated by real time processing.The pro-jection-based algorithm reported here has the potential of al-leviating much of the computational burden of image registra-tion by operating only on vectors as opposed to images.The proposed technique also has the benefit of being more robust against temporal and especially fixed-pattern noise,which can be a great impediment to achieving accurate image registration.In this paper,the performance of the projection-based shift es-timator is compared to the performance of the 2-D cross-corre-lation shift estimator.A significant conclusion drawn from this work is that the relative performance of the two estimators varies depending on the auto-correlation function of the scene.The two auto-correlation models considered in the analysis show the diverse dependence of these algorithms on the degree of spatial correlation in the image.Scenes that possess signifi-cant neighbor-to-neighbor correlation over large neighborhoods will allow the projection-based shift estimator to perform better than the 2-D cross-correlator,while scenes whose pixels are un-correlated allow the 2-D cross-correlator to produce superior re-sults.Both estimators work well when the standard deviation of the noise is small with respect to the contrast in the scene.The decision of which estimator to use would be a simple one at high SNRs because the projection-based technique,as described in Section II,operateson pixels in comparisonto.With thisauto-correlation model,the expressionforthatminimizesis minimizedif1870IEEE TRANSACTIONS ON IMAGE PROCESSING,VOL.10,NO.12,DECEMBER2001Fig.10.(Top)Two sample frames from a sequence of video of the planet Jupiter,(bottom left)frame one of the sequence at a PSNR of 1,and (bottom right)PSNR of 10.the numerator gives rise to the followinginequality:Next,we wish to maximize the denominator or determine an upper bound for it.Because all the terms in the denominator arestrictly positive,if we choose them to be as large as possible,this will serve as an upper bound for the denominator.If all the impulse functions take on their maximum value,then the following inequalityresults:CAIN et al.:PROJECTION-BASED IMAGE REGISTRATION IN PRESENCE OF FIXED-PATTERN NOISE1871Fig.11.Root mean-squared registration error performance of the projection-based technique (dotted line)and the 2-D cross-correlator (solid line)as a function of peak temporal SNR for the Jupiter datasequence.Fig.12.Root mean-squared registration error performance of the projection-based technique (dotted line)and the 2-D cross-correlator (solid line)as a function of peak fixed-pattern SNR for the Jupiter data sequence.For the projection-based estimator,the vertical-direction figure-of-merit can be computed and expressedas,re-sultsinand ,then the numerator isminimized.Because all the terms in the denominator are non-negative,if we choose the impulse functions to be equal to zero,then this will serve as a lower bound for the value of the de-nominator.Now that the numerator is as large as possible andthe denominator is as small as possible,the figure-of-merit is bounded above in the followinginequality:.This means thatthe inequality can be reduced to the followingexpression:If the ratio of the figures-of-merit is greater than one then the denominator subtracted from the numerator should be greater thanzero,if1872IEEE TRANSACTIONS ON IMAGE PROCESSING,VOL.10,NO.12,DECEMBER2001 [13] A.Schaum and M.McHugh,“Analytic methods of image registration:Displacement estimation and resampling,”,Naval Res.Rep.9298,Feb.1992.[14]T.S.Huang,Image Sequence Analysis.Berlin,Germany:Springer-Verlag,1981.[15]S.C.Cain and K.D.Sauer,“Fast block motion estimation using integralprojections,”presented at the IEEE Workshop Visual Communicationsand Signal Processing,Raleigh,NC,1992.[16]K.Sauer and B.Schwartz,“Efficient block motion estimation using in-tegral projections,”IEEE Trans.Circuits Syst.Video Technol.,vol.6,pp.513–518,Oct.1996.[17]J.S.Kim and R.H.Park,“A fast feature-based block matching algorithmusing integral projections,”IEEE J.Select.Areas Commun.,vol.10,pp.968–971,June1992.[18] A.R.Hambley,Introduction to Communication Systems.Rockville,MD:Computer Science,1990.[19]H.Stark and J.W.Woods,Probability,Random Processes,and Estima-tion Theory for Engineers.Englewood Cliffs,NJ:Prentice-Hall,1994.[20]M.C.Roggemann and B.Welsh,Imaging Through Turbulence.BocaRaton,FL:CRC,1996.Stephen C.Cain was born in Pontiac,MI,in 1969.He received the B.S.E.E.degree from the University of Notre Dame,Notre Dame,IN,in1992, the M.S.E.E.degree from Michigan Technological University,Houghton,in1994,and the Ph.D.degree in electrical engineering from the University of Dayton,Dayton,OH,in2001.From1994to1997,he served as an Officer in the United States Air Force.From1997to1998,he was with Wyle Laboratories and from1998to2000,he was with ITT A/CD.In2000,he began teaching asan Visiting Assistant Professor in the Department of Engineering,Purdue Uni-versity at Fort Wayne,Fort Wayne,IN,where he now teaches as an AssistantProfessor.Majeed M.Hayat(S’89-M’92-SM’00)was bornin Kuwait in1963.He received the B.S.degree(summa cum laude)in electrical engineering fromthe University of the Pacific,Stockton,CA,in1985,and the M.S.degree in electrical engineering andthe Ph.D.degree in computer engineering,from theUniversity of Wisconsin-Madison in1988and1992,respectively.From1993to1996,he was with the Universityof Wisconsin-Madison as a Research Associate andCo-Principal Investigator of a project on statistical minefield modeling and detection,which was funded by the Office of Naval Research.In1996,he joined the faculty of the Electro-Optics Graduate Pro-gram and the Department of Electrical and Computer Engineering,University of Dayton,Dayton,OH,where he was granted early tenure and promotion to As-sociate Professor in2000.He is currently an Associate Professor in the Depart-ment of Electrical and Computer Engineering at the University of New Mexico, Albuquerque.His research interests include modeling and design of high-per-formance photodetectors,optical communication systems,statistical communi-cation theory,communication networks,infrared imaging,and statistical signal and image processing.Dr.Hayat is a recipient of a1998National Science Foundation Early Faculty Career Award.He is a member of SPIE andOSA.Ernest E.Armstrong received the B.S.degreein computer science and the M.S.degree in X-raycrystallography from Wright State University,Dayton,OH,in1977and1980,respectively,andreceived the Ph.D.degree in material analysis fromCase Western Reserve University,Cleveland,OH.He is currently a Senior Scientist(image pro-cessing)for OptiMetrics,Inc.,a defense contractorat Wright Patterson AFB,OH.He previously heldpositions with Exxon Production Research andBritish Petroleum Research,where his research was primarily concerned with the application of signal and image processing techniques in materials analysis.He has authored and coauthored a number of publications in image processing,specializing in the area of image registration, resolution enhancement,and nonuniformity correction.。

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活动所需物品清单 英语作文

活动所需物品清单 英语作文

活动所需物品清单英语作文An Exhaustive Inventory for a Memorable Event.Organizing an event, whether it's a grand celebration, an intimate gathering, or a corporate function, requires meticulous planning and a comprehensive inventory of essential items. From the venue to decorations, catering to entertainment, every detail contributes to the overall success and enjoyment of the occasion. To ensure a seamless and unforgettable experience, it is imperative to compile an exhaustive checklist that encompasses all the necessary components.Venue and Ambiance.Venue: Secure an appropriate venue that aligns with the event's size, nature, and target audience. Consider factors such as capacity, accessibility, amenities, and ambiance.Lighting: Set the mood and enhance the ambiance with appropriate lighting fixtures. Choose from spotlights, chandeliers, dimmers, and colored gels to create the desired atmosphere.Sound System: Ensure clear and ample sound for speeches, announcements, and entertainment. Rent or hire a sound system that meets the venue's specifications and the event's requirements.Seating: Provide comfortable seating arrangements for guests, considering the event's duration and formality. Options include chairs, sofas, benches, and tables.Decorations: Transform the venue with tasteful decorations that reflect the event's theme, color scheme, and overall ambiance. Utilize balloons, streamers, floral arrangements, centerpieces, and artwork to create a visually appealing space.Catering and Refreshments.Food: Plan a delectable menu that caters to thedietary preferences and tastes of your guests. Consider appetizers, main courses, desserts, and late-night snacks.Beverages: Offer a variety of beverages, includingnon-alcoholic drinks, beer, wine, cocktails, and specialty beverages. Ensure an ample supply of ice and glassware.Bar Service: Hire or arrange for professional bar service to ensure smooth and efficient beverage distribution. Provide sufficient bar staff and equipment to handle the anticipated crowd.Tables and Linens: Rent or purchase tables and linens that complement the venue's decor and create an inviting dining atmosphere.Serving Ware: Acquire serving ware, including plates, utensils, napkins, glasses, and serving bowls, insufficient quantities to accommodate all guests.Entertainment and Activities.Entertainment: Engage the guests with captivating entertainment options. Hire musicians, comedians, DJs, or performers who align with the event's theme and audience.Activities: Plan interactive activities that encourage guest participation and foster a lively atmosphere. Consider games, photo booths, art installations, or workshops.Technology: Enhance the event experience with technological enhancements. Set up projection screens, microphones, or interactive displays for presentations, speeches, or entertainment.Master of Ceremonies (MC): Hire or appoint a professional MC to emcee the event, introduce speakers, and keep the program flowing smoothly.Logistics and Administration.Event Staff: Hire or recruit a team of event staff toassist with setup, coordination, guest management, and cleanup.Registration and Check-In: Set up a registration and check-in area to welcome guests, distribute materials, and collect any necessary information.Security: Ensure the safety and security of guests, staff, and the venue by hiring security personnel or coordinating with local authorities.First Aid and Medical Assistance: Provide access to basic first aid and medical assistance in case of emergencies.Transportation: Arrange transportation for guests if the venue is not easily accessible by public transportation. Consider buses, shuttles, or ride-sharing services.Other Essential Items.Signage: Create clear and visible signage to guideguests and provide important information, such as directions, restrooms, and emergency exits.Restrooms: Ensure the availability of clean and well-maintained restrooms for guests.Waste Management: Arrange for waste disposal bins and designate areas for trash and recycling.Contingency Plan: Develop a contingency plan in case of unforeseen events, such as inclement weather, technical difficulties, or emergencies.Final Touches.Timeline: Create a detailed timeline for the event, including setup, program, and cleanup.Budget: Establish a realistic budget for the event and track expenses diligently.Guest List: Finalize the guest list and send outinvitations with clear instructions regarding RSVP, dress code, and any special requests.Communication: Keep guests and vendors informed throughout the planning process and provide regular updates as needed.Follow-Up: After the event, send thank-you notes to guests and vendors, and evaluate the event's success to identify areas for improvement in future endeavors.By meticulously compiling an exhaustive inventory and addressing every aspect of the event, organizers can ensure a seamless and memorable experience for all attendees. From the initial planning stages to the final cleanup, a comprehensive inventory serves as a roadmap for success, ensuring that every detail is accounted for and executed flawlessly.。

ENVI-的图像配准

ENVI-的图像配准

请教对ETM+进行几何校正的详细步骤？阎殿武ENVI 的图像配准与几何校正工具允许你将图像定位到地理坐标上，并校正它们使其与基图像几何形状相匹配。

图像可以用 Rotate/Flip Data 菜单项在配准以前进行旋转。

通过使用全分辨率(主图像) 和缩放窗口选择地面控制点（GCPs），来进行图像－图像和图像－地图的配准。

基图像和未校正图像的GCPs 的坐标被显示，伴随有特定纠正算法计算的误差项。

纠正用重采样、缩放和平移，多项式函数或德洛内三角测量（RST）实现。

支持的重采样方法包括最近邻、双线性和立方体卷积。

用 ENVI 的多个动态覆盖能力，对基图像和纠正图像进行比较，可以快速估价配准精度。

参阅 ENVI Tutorial Image Georeferencing and Registration 中有关图像配准的详细描述。

镶嵌允许多个图像插入到一个用户定义了大小和坐标的基图像中。

独立图像或多波段图像文件被输入，且放到图像或地图坐标中或用鼠标确定位置。

输出的镶嵌特征可以用图幅显示，且能进行交互式调整。

用 ENVI 可以进行虚拟镶嵌，这使你不必将数据的两个副本存到磁盘上。

羽化技术能用于混合图像边界，进行无缝镶嵌。

镶嵌模板可以被存储，用于其它图像。

ENVI 配准、校正和镶嵌功能可以从 ENVI 的主菜单中的 Register 菜单里选择。

Select Ground Control Points (选择地面控制点)在菜单 Register 里的Select Ground Control Points菜单项允许交互式选择地面控制点（GCPs），并对单一波段图像或多波段文件纠正。

这一工具允许对不同控制点和纠正项进行原形恢复和检验。

支持图像-图像和图像-地图的配准。

详细步骤参见 ENVI Tutorial Image Georeferencing and Registration 中的图像配准例子。

Image-to-Image Registration (图像-图像的配准)图像-图像配准需要两幅图像均打开。

实用英语词汇系列：测绘类翻译词汇_Part3

实用英语词汇系列：测绘类翻译词汇_Part3

水位分带改正correction of tidal zoning水位改正correction of water level水位改正correction of water level水位曲线curves of water level水位曲线curves of water level水位遥报仪communication device of water level水位遥报仪communication device of water level水文观测，*水文测验 hydrometry水文要素 hydrologic features水下摄影测量 underwater photogrammetry水下摄影机 underwater camera水准测量 leveling surveying水准尺 leveling staff水准点 Benchmark水准路线 leveling line水准面 level surface水准器 Bubble水准网 leveling network水准仪，*水准器 level水准原点 leveling origin瞬间地图 twinkling map瞬时极 instantaneous pole瞬时视场 IFOV瞬时视场instantaneous field of view丝网印刷 silk-screen printing斯托克斯公式 Stokes formula斯托克斯理论 Stokes theory撕膜片 peel-coat film四色印刷four color printing似大地水准面 quasi-geoid搜索区 searching area岁差 precession隧道测量 tunnel survey穗帽变换tasseled cap transformation缩微地图 microfilm map缩微摄影 microcopying缩微摄影 microphotography缩小仪 photoreducer塔尔科特测纬度法Talcott method of latitude determination 台链 station chain太阳辐射波谱solar radiation spectrum太阳光压摄动solar radiation pressure perturbation太阳同步卫星 sun-synchronous satellite态势地图 posture map特殊水深 special depth特征 feature特征编码 feature coding特征码 feature codes特征码清单 feature codes menu特征提取 feature extraction特征选择 feature selection特种地图 particular map体素 voxel天波干扰 sky-wave interference天波修正 sky-wave correction天顶距 zenith angle天顶距 zenith distance天球坐标系celestial coordinate system天球坐标系celestial coordinate system天文大地垂线偏差astro-geodetic deflection of the vertical天文大地网 astro-geodetic network天文大地网平差Adjustment of astrogeodetic network天文点 astronomical point天文定位系统 astronomical positioning system天文方位角 astronomical azimuth天文经度 astronomical longitude天文经纬仪 astronomical theodolite天文年历 astronomical almanac天文年历 astronomical ephemeris天文水准 astronomical leveling天文纬度 astronomical latitude天文重力水准 astro-gravimetric leveling天文坐标测量仪astronomical coordinate measuring instrument 天线高度 antenna height田谐系数coefficient of tesseral harmonics田谐系数coefficient of tesseral harmonics条幅[航带]摄影机 continuous strip camera条幅[航带]摄影机 continuous strip camera条幅[航带]摄影机 strip camera条件方程 condition equation条件方程 condition equation条件平差 condition adjustment条件平差 condition adjustment铁路工程测量railroad engineering survey通用横墨尔卡投影 Universal Transverse Mercator projection 通用极球面投影Universal Polar Stereographic projection通用极球面投影 UPS通用墨卡尔投影 UTM同步观测 simultaneous observation同步验潮 tidal synobservation同名光线corresponding image rays同名光线corresponding image rays同名核线corresponding epipolar line同名核线corresponding epipolar line同名像点 corresponding image points同名像点 corresponding image points同名像点 homologous image points统计地图 statistic map投影变换 projection transformation投影差 height displacement投影差 relied displacement投影方程 projection equation投影器 Projector投影器主距principal distance of projector投影晒印 projection printing透光率 transmittance透明负片 transparent negative透明正片 transparent positive透明注记 stick-up lettering透视截面法 perspective traces透视投影 perspective projection透视旋转定律，*沙尔定律 Chasles theorem透视旋转定律，*沙尔定律 Chasles theorem透视旋转定律，*沙尔定律rotation axiom of the perspective 透视旋转定律，*沙尔定律 rotational theorem图幅 mapsheet图幅编号 sheet designation图幅编号 sheet number图幅接边 edge matching图幅接合表 index diagram图幅接合表 sheet index图根点 mapping control point图根控制 mapping control图解纠正 graphical rectification图解图根点 graphic mapping control point图廓edge of the format图廓 map border图历簿mapping recorded file图例 legend图面配置 map layout图象 picture图像编码 image coding图像变换 image transformation图像处理 image processing图像分割 image segmentation图像分析 image analysis图像复合 image overlaying图像几何纠正geometric rectification of imagery图像几何配准geometric registration of imagery图像理解 image understanding图像描述 image description图像识别 image recognition图像数字化 image digitization图像增强 image enhancement图形 graphics图形-背景辨别 F-G discrimination图形-背景辨别 Figure-ground discrimination图形符号 graphic symbol图形记号 graphic sign图形权倒数weight reciprocal figure图形元素 graphic elements土地规划测量 land planning survey土地利用现状图 present landuse map土地信息系统 land information system土地信息系统 LIS推荐航线 recommended route托帕可斯卫星 T/P托帕克斯卫星 TOPEX/POSEIDON拖底扫海 aground sweeping陀螺定向光电测距导线gyrophic EDM traverse陀螺方位角 gyro azimuth陀螺经纬仪 gyro theodolite陀螺经纬仪 gyroscopic theodolite陀螺仪定向测量 gyrostatic orientation survey椭球扁率 flattening of ellipsoid椭球长半轴，*地球长半轴semimajor axis of ellipsoid 椭球短半轴，*地球短半轴semiminor axis of ellipsoid 椭球面大地测量学 ellipsoidal geodesy椭球偏心率 eccentricity of ellipsoid拓扑地图 topological map拓扑关系 topological relation拓扑检索 topological retrieval外部定向 exterior orientation网点 stipple网格地图 grid map网格法 grid method网格结构 grid structure网屏 screen网纹片 transparent foil网线 ruling危险界限 limiting danger line微波测距仪microwave distance measuring instrument微波辐射 microwave radiation微波辐射计 microwave radiometer微波图像 microwave imagery微波遥感microwave remote sensing微波遥感器microwave remote sensor微重力测量 microgravimetry维纳频谱 Winer spectrum维宁•曼尼斯公式 Vening-Meinesz formula伪彩色图像 pseudo-color image伪等值线地图 pseudo-isoline map伪距测量 pseudo-range measurement卫星测高 satellite altimetry卫星大地测量 satellite geodesy卫星定位 satellite positioning卫星多普勒[频移]测量satellite Doppler shift measurement卫星多普勒定位 satellite Doppler positioning卫星高度 satellite altitude卫星跟踪卫星技术 satellite-to-satellite tracking卫星跟踪卫星技术 SST卫星跟踪站satellite tracking station卫星共振分析analysis of satellite resonance卫星构形 satellite configuration卫星-惯导组合定位系统satellite-inertial guidance integrated positioning sy卫星轨道改进improvement of satellite orbit卫星激光测距satellite laser ranging卫星激光测距，侧视雷达 SLR卫星激光测距仪satellite laser ranger卫星-声学组合定位系统satellite-acoustics integrated positioning system卫星受摄运动perturbed motion of satellite卫星像片图 satellite photo map卫星星下点 sub-satellite point卫星运动方程equation of satellite motion卫星重力梯度测量 satellite gradiometry卫星姿态 satellite attitude位置[线交]角intersection angle of LOP位置函数，*坐标函数 position function位置精度 positional accuracy位置线 line of position位置线 LOP位置线方程equation of LOP文化地图 cultural map文化地图 cultural map纹理分析 texture analysis纹理增强 texture enhancement沃尔什变换 Walsh transformation无线电定位 radio positioning无线电航行警告 radio navigational warning无线电指向标，*电指向 radio beacon无线电指向标表list of radio beacon五角棱镜 pentaprism物镜分辨力resolving power of lens物理大地测量学，*大地重力学 physical geodesy 误差检验 error test误差理论theory of errors误差椭圆 error ellipse雾[信]号 fog signal系列地图 series maps系统集成 system integration系统误差 systematic error弦线支距法 Chord off-set method弦线支距法 Chord off-set method显微摄影 photomicrography现势地图 up-to-data map线路平面图 route plan线路水准测量 route leveling线路中线测量 center line survey线路中线测量 center line survey线路中线测量 location of route线纹米尺，*日内瓦尺 standard meter线形锁 linear triangulation chain线形网 linear triangulation network线性调频脉冲 Chirp线性调频脉冲 Chirp线阵遥感器linear array sensor线阵遥感器 pushbroom sensor线状符号 line symbol限差 tolerance限航区 restricted area乡村规划测量 rural planning survey相对定位 relative positioning相对定向 relative orientation相对定向元素element of relative orientation相对航高 relative flying height相对论改正 relativistic correction相对误差 relative error相对重力测量 relative gravity measurement相干声呐测深系统interferometric seabed inspection sonar相关平差Adjustment of correlated observation相关器 correlator相关器 correlator相位传递函数 phase transfer function相位传递函数 PTF相位多值性 phase ambiguity相位模糊度解算 phase ambiguity resolution相位漂移 phase drift相位稳定性 phase stability相位周，*巷 lane相位周，*巷 phase cycle相位周值，*巷宽 lane width相位周值，*巷宽 phase cycle value镶嵌索引图 index mosaic巷道验收测量footage measurement of workings象限仪 quadrant象形符号 replicative symbol像场角angular field of view像等角点 isocenter of photograph像底点photo nadir point像地平线，*合线 horizon trace像地平线，*合线 image horizon像地平线，*合线 vanishing line像幅 picture format像空间坐标系image space coordinate system像片 photo像片 photograph像片比例尺 photo scale像片地质判读，*像片地质解译geological interpretation of photograph 像片方位角 azimuth of photograph像片方位元素photo orientation elements像片基线 photo base像片纠正 photo rectification像片内方位元素elements of interior orientation像片判读 photo interpretation像片平面图 photoplan像片倾角tilt angle of photograph像片外方位元素elements of exterior orientation像片镶嵌 photo mosaic像片旋角 swing angle像片旋角 yaw像片主距principal distance of photo像平面坐标系photo coordinate system像移补偿image motion compensation像移补偿 IMC像元 pixel像主点principal point of photograph像主纵线principal line [of photograph]销钉定位法 stud registration小潮升 neap rise小潮升 neap rise小角度法minor angle method小像幅航空摄影 SFAP小像幅航空摄影small format aerial photography 协调世界时 coordinate universal time协调世界时 coordinate universal time协调世界时 UTC协调世界时时号time signal in UTC协方差函数 covariance function协方差函数 covariance function心象地图 mental map新版海图new edition of chart新版海图new edition of chart信号杆 signal pole信息量 contents of information信息量 contents of information信息提取 information extraction信息属性 information attribute星载遥感器 satellite-borne sensor行差 run error行星大地测量学 planetary geodesy行政区划图 administrative map修版 retouching虚地图 virtual map虚拟地景 virtual landscape序惯平差 sequential adjustment悬式经纬仪 hanging theodolite旋转参数 rotation parameters选取限额norm for selection选取限额norm for selection选取指标index for selection选权迭代法iteration method with variable weights寻北器 north-finding instrument寻北器 north-finding instrument寻北器 polar finder压力验潮仪 pressure gauge亚太区域地理信息系统基础设施常设委员会 PCGIAP亚太区域地理信息系统基础设施常设委员会 Permanent Committee on GIS Infrastructure for Asia and the Pacific严密平差 rigorous adjustment沿海测量 coastwise survey沿海测量 coastwise survey颜色空间 color space颜色空间 color space验潮 tidal observation验潮仪 tide-meter验潮站 tidal station验潮站零点zero point of the tidal阳像 positive image遥感 remote sensing遥感测深 remote sensing sounding遥感模式识别pattern recognition of remote sensing遥感平台 remote sensing platform遥感数据获取remote sensing data acquisition遥感制图 remote sensing mapping野外地质图field geological map野外填图 field mapping因瓦基线尺invar baseline wire阴像 negative image阴像 negative image引潮力 tide-generating force引潮位 tide-generating potential引航图集 pilot atlas引力 gravitation引力位 gravitational potential引水锚地 pilot anchorage引张线法method of tension wire alignment印刷版 printing plate荧光地图 fluorescent map影像 image影像 imagery影像地质图 geological photomap影像分辨力 image resolution影像分辨力resolving power of image影像复原 image restoration影像金字塔 image pyramid影像匹配 image matching影像融合 image fusion影像数据库 image database影像相关 image correlation影像镶嵌 image mosaic影像质量 image quality游艇用图 smallcraft chart游艇用图 yacht chart渔礁 fishing rock渔堰 fishing haven渔业用图 fishing chart渔栅 fishing stake宇宙制图 cosmic mapping宇宙制图 cosmic mapping预报地图 Prognostic map预打样图 pre-press proof预制符号 preprinted symbol预制感光板，*PS 版 presensitized plate原子钟 atomic clock圆曲线测设 circular curve location圆曲线测设 circular curve location圆-圆定位，*距离-距离定位 range-range positioning 圆柱投影 cylindrical projection圆柱投影 cylindrical projection圆锥投影 conic projection圆锥投影 conic projection远程定位系统long-range positioning system远海测量 pelagic survey月平均海面monthly mean sea level月球轨道飞行器 lunar orbiter运动方程分析解analytical solution of motion equation 运动方程数值解numerical solution of motion equation 运动方程数值解numerical solution of motion equation 运动线法 Arrowhead method晕滃法 hachuring晕渲法 hill shading载波相位测量 carrier phase measurement载波相位测量 carrier phase measurement再分结构 subdivisional organization凿井施工测量construction survey for shaft sinking凿井施工测量construction survey for shaft sinking栅格绘图 raster plotting栅格数据 raster data站心坐标系topocentric coordinate system章动 nutation章动 nutation照相排字机 phototypesetter照相制版镜头 printer lens照相制版镜头 process lens照准点 sighting point照准点归心 sighting centring真地平线，*真水平线 true horizon真实孔径雷达 real-aperture radar真误差 true error真子午线 true meridian整体大地测量 integrated geodesy整体感 associative perception整体结构 extensional organization正常高 normal height正常高 normal height正常水椭球，*水准椭球 normal level ellipsoid 正常水椭球，*水准椭球 normal level ellipsoid 正常引力位 normal gravitation potential正常引力位 normal gravitation potential正常重力 normal gravity正常重力 normal gravity正常重力场normal gravity field正常重力场normal gravity field正常重力公式normal gravity formula正常重力公式normal gravity formula正常重力位normal gravity potential正常重力位normal gravity potential正常重力线normal gravity line正常重力线normal gravity line正方形分幅 square mapsubdivision正片 positive正象 right-reading正直摄影normal case photography正直摄影normal case photography正轴投影 normal projection正轴投影 normal projection郑和航海图Zheng He's Nautical Chart政治地图 political map支水准路线 spur leveling line直方图规格化 histogram specification直方图均衡 histogram equalization直角坐标网 rectangular grid志田数 Shida'a number制图分级 cartographic hierarchy制图分级 cartographic hierarchy制图简化 cartographic simplification制图简化 cartographic simplification制图精度 mapping accuracy制图夸大 cartographic exaggeration制图夸大 cartographic exaggeration制图专家系统cartographic expert system制图专家系统cartographic expert system制图资料 cartographic document．制图资料 cartographic document．制图资料 source material 质底法quality base method质量感 qualitative perception秩亏平差rank defect adjustment置信度 Confidence置信度 Confidence中程定位系统medium-range positioning system中国测绘学会Chinese Society of Geodesy, Photogrammetry and Cartog中国测绘学会 CSGPC中国测绘学会Chinese Society of Geodesy, Photogrammetry and Cartog中国测绘学会 CSGPC中国大地测量星表 CGSC中国大地测量星表Chinese Geodetic Stars Catalogue中国大地测量星表 CGSC中国大地测量星表Chinese Geodetic Stars Catalogue中华人民共和国测绘法Surveying and Mapping Law of the People's Republic of中天法 transit method中误差 RMSE中误差root mean square error中心式快门 between-the-lens shutter中心式快门 lens shutter中星仪 transit instrument中性色调，*灰色调 middle tone中央子午线 central meridian中央子午线 central meridian钟偏 clock offset钟偏 clock offset钟速 clock rate钟速 clock rate重采样 resampling重力 gravity重力测量 gravity measurement重力场 gravity field重力潮汐改正correction of gravity measurement for tide 重力潮汐改正correction of gravity measurement for tide 重力垂线偏差gravimetric deflection of the vertical重力垂直梯度vertical gradient of gravity重力点 gravimetric point重力固体潮观测gravity observation of Earth tide重力归算 gravity reduction重力基线 gravimetric baseline重力基准 gravity datum重力数据库 gravimetric database重力水平梯度horizontal gradient of gravity重力梯度测量gradiometry重力梯度测量gravity gradient measurement重力梯度仪 gradiometer重力位 gravity potential重力仪 gravimeter重力异常 gravity anomaly周期误差 periodic error周跳 cycle slip周跳 cycle slip轴颈误差error of pivot主垂面principal plane [of photograph]主垂面principal vertical plane主动式遥感 active remote sensing主分量变换 Principal component transformation主合点principal vanishing point主核面principal epipolar plane主核线principal epipolar line主检比对 main/check comparison主台 main station主轴线测设setting-out of main axis属性精度 attribute accuracy助曲线，*辅助等高线 extra contour专题测图仪 thematic mapper专题测图仪 TM专题层 thematic overlap专题地图 thematic map专题地图集 thematic atlas专题地图学 thematic cartography专题海图 thematic chart专用地图special use map转点仪，*制点仪point transfer device转绘仪 sketchmaster状态向量 state vector准确度 Accuracy姿态 Attitude姿态参数 attitude parameter姿态测量遥感器 attitude-measuring sensor资源与环境遥感remote sensing for natural resources and environment 子午面 meridian plane子午圈 meridian子午圈曲率半径radius of curvature in meridian自动安平水准仪 automatic level自动安平水准仪 compensator level自动安平水准仪 compensator level自动化地图制图 automatic cartography自动绘图 automatic plotting自动空中三角测量 automatic triangulation自动坐标展点仪 automatic coordinate plotter自检校 self-calibration自然地图 physical map自准直目镜 autocollimating eyepiece综合测绘系统general surveying system综合地图 comprehensive map综合地图 comprehensive map综合地图集 comprehensive atlas综合地图集 comprehensive atlas综合法测量photo planimetric method of photogrammetric mapping纵断面测量 profiles survey纵断面图 Profile纵断面图 profile diagram组合地图 homeotheric map组合定位 integrated positioning钻孔位置测量 bore-hole position survey最大似然分类 maximum likelihood classification最小二乘法 least square method最小二乘配置法，*最小二乘拟合推估法least squares collocation最小二乘相关least squares correlation最小距离分类 minimum distance classification左右视差 horizontal parallax左右视差 x-parallax坐标地籍 coordinate cadastre坐标地籍 coordinate cadastre坐标方位角 grid bearing坐标格网 coordinate grid坐标格网 coordinate grid坐标量测仪coordinate measuring instrument坐标量测仪coordinate measuring instrument坐标增量increment of coordinate坐标增值闭合差closing error in coordinate increment 坐标增值闭合差closing error in coordinate increment 坐标中误差mean square error of coordinate。

图像自动配准

图像自动配准

图像自动配准目录图像自动配准 (1)1.概述 (2)2.详细操作步骤 (2)2.1相同分辨率影像的图像配准 (3)第一步：选择图像配准的文件 (3)第二步：生成Tie点 (4)2.2不同分辨率影像的图像配准 (10)1.概述经常在实际数据生产中会遇到，同一地区的图像或者相邻地区有重叠区的图像，由于几何校正误差的原因，重叠区的相同地物不能重叠，这种情况对图像的融合、镶嵌、动态监测等应用带来很大的影响。

遇到这种情况，可以利用重叠区的匹配点和相应的计算模型进行精确配准。

图像配准(Image Registration)就是将不同时间、不同传感器（成像设备）或不同条件下（天候、照度、摄像位置和角度等）获取的两幅或多幅图像进行匹配、叠加的过程。

本课程学习使用Image Registration Workflow对两幅几何位置有偏差的影像进行配准。

Image Registration Workflow，它是自动、准确、快速的影像配准工作流，将繁杂的参数设置步骤集成到统一的面板中，在少量或者无需人工干预的情况下能快速而准确的实现影像间的自动配准。

练习数据包括一对相同分辨率、不同成像时间的数据，一对不同分辨率、相同成像时间的全色和多光谱数据，具体情况如下：文件说明01-b.img（.hdr）、02-b.img（.hdr） 2.5米空间分辨率、不同成像时间的影像数据多光谱图像.dat（.hdr）、全色图像.dat（.hdr）2.1米全色、6.8米多光谱的影像数据2.详细操作步骤Image Registration Workflow工具需要两幅影像，其中一幅是基准影像，另一幅则是待配准影像。

配准工具支持的数据格式有：●ENVI,TIFF,NITF,JPEG2000,JPEG,Esri®raster layer。

●Geodatabase raster。

●Web Services。

基准图像必须包括标准的地图坐标或者RPC信息，不能是像素坐标、有坐标没有投影信息（arbitrary坐标信息）和伪坐标（pseudo）；待配准影像没有严格约束，但如果没有坐标信息，需要手动选择至少3个同名点。