Andersen, Simon G.

Vestergaard, Jacob Schack

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Normalization of ortophotos based on no-change pixels using Multivariate AlterationDetection (MAD)Vestergaard, Jacob Schack; Andersen, Simon G.Publication date:2010Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Vestergaard, J. S., & Andersen, S. G. (2010). Normalization of ortophotos based on no-change pixels usingMultivariate Alteration Detection (MAD). Poster session presented at DTU Visiondays Conference 2010,Kgs.Lyngby, Denmark.Normalization of ortophotos based on no-change pixelsusing Multivariate Alteration Detection (MAD)Jacob S. Vestergaard, Simon G. AndersenDTU Informatics, Technical University of DenmarkIntroductionMosaicing of images recorded by aerial photography is a dis-cipline with many challenges: collecting the data, choos-ing ortophoto model, placement of the seamline and colorcorrection. All must be perfected in order to create good-looking maps for e.g. Google Maps. Still, large amount ofthis work is carried out using outsourced manual labour. Au-tomated methods should be developed in order to in-sourcethe work.In this project we aim to colorcorrect 16 ortophotos provided byCowi A/S. The standard method forcorrecting the colors is to histogramequalize two images to each other.However, this runs the risk of usingcolor information from pixels wherea change has occured in the over-lap since last recording, e.g. a carcaught by the camera.By applying a multivariate statisti-cal change detection method to de-tect pixels where no change has oc-curred a more robust method fornormalization of ortophotos is ob-tained.Acknowledgements to Anders T.Rasmussen [4].Ortophotos..20%.60%.1 .2.8Collected by Cowi A/S. Differencesin color and intensity are primarilycaused by changed flight directionand time of day. 16 ortophotos 3 channels: R, G, B 14650  9560  140 MP 60% overlap in fligh direction 20% overlap of lanesFigure 1: Ortophotos from Lake TystrupDetecting no-change pixels with MADFigure 2: Overlaps with artificial red andblue cars. MAD transformation detects bothcars and changed lighting conditions onhouses.Multivariate Alteration Detection (MAD) is a statistical method for change detectionin bi-temporal, multi- and hyperspectral data [2].This method is based on Canonical Correlation Analysis (CCA) [1]. CCA searchesfor linear combinations U = aTX and V = bTY of the (ideally) Gaussian distributedvariables [X;Y] 2 N (;) with maximum correlation = Corr [U;V] =Cov [U;V]pVar[U]Var[V]=aT12bqaT11a bT22b: (1)The MAD transformation [3] is defined asXY !2664aTpX  bTpY:::aT1 X  bT1 Y3775 (2)which is seen to be the subtraction of the canonical variates in reverse ordering.No-change pixel j approximately follows a 2 distribution:. .x..23.Tj =pXi=1 MADijMADi!22 2(p) :1% most probable no-change pixels used fornormalization.Normalizing..x2.x1Figure 3: Ordinary Least Squares regression..x2.x1Figure 4: Orthogonal regressionThe no-change pixels found in the overlap are arranged in 3N matrices.One of the sets is chosen as reference I2. A transformation A with anoffset that adjusts the other set I1 to the reference is approximated.24 a b c d e f g h i 3526664r11    r1Ng11    g1Nb11    b1N1    137775 =24r21    r2Ng21    g2Nb21    b2N35A I1 I2The diagonal is heavily dominating the transformation matrix.Ordinary Least Squares (OLS) regression seeks to minimize the ver-tical distance between an input variable and the regression line. Thisimplicitly assumes that one of the variables is error free. Here, the refer-ence variable is chosen arbitrarily and therefore error must be assumedon both variables.Orthogonal Regression (OR) seeks to minimize the orthogonal distancebetween the observed datapoint and the regression line. Hereby an equalweigthing of errors on both variables is achieved.ResultsBefore OLS ORNo-change pixels 1.28e3 310 (76%) 378 (71%)Overlap 1.83e5 1.28e5 (30%) 1.44e5 (21%)Table 1: Residual sums of squaresA visible improvement of the color correspondenceafter normalization.Orthogonal regression performs better than OLSregression visually, but not numerically.Change of scene, caused by different camera an-gles and new items, detected in overlap using MAD. Large color changes within overlap provides poorresults.Original OLS normalization OR normalizationConclusionsA set of images provided by Cowi A/S have been normalizedfor better color correspondence.No-change pixels in the overlaps have been used as refer-ence for the normalization. The multivariate method MADhas been implemented to detect no-change pixels in theoverlap. Ordinary Least Squares and Orthogonal Regressionmethods have been evaluated.Visible improvments have been achieved from the originalimages to the normalized images.Future work includes distance maps to use more overlaps,determination of a normalization sequence and implement-ing methods for noise filtering, e.g. Markov Random Fields.[1] Harold Hotelling. Relations between two sets of variates. Biometrika, 28(3/4):321--377, 1936.[2] A. A. Nielsen. The regularized iteratively reweighted MAD method for change detection in multi- andhyperspectral data. IEEE Transactions on Image Processing, 16(2):463--478, feb 2007.[3] A. A. Nielsen, K. Conradsen, and J. J. Simpson. Multivariate alteration detection (MAD) and MAF post-processing in multispectral, bitemporal image data: New approaches to change detection studies.Remote Sensing of Environment, 64(1):1--19, 1998.[4] Anders Thirsgaard Rasmussen. Color adjustment of orthophotos. Master's thesis, Technical Univer-sity of Denmark, Denmark, June 2010.

Normalization of ortophotos based on no-change pixels using Multivariate Alteration Detection (MAD)

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Normalization of ortophotos based on no-change pixels using Multivariate Alteration Detection (MAD)

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