12.1.5 Change Detection -- Image Level

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Allen, G.R., Bonrud, L.O., Cosgrove, J.J., and Stone, R.M.,
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Lillestrand, R.L.,
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TC(21), No. 7, July 1972, pp. 654-659. Change Detection, Differencing. This work at Control Data Corp. took two real images as input, warped one to corresponds to the other spatially, and transformed the intensity values to account for wide area variations. Subtraction of the images indicated regions of changes. This work involved the development of real-time special purpose systems to perform the matching, warping, and differencing for change detection in a variety of imagery domains (X-ray, radar, and visible light). Also transform regions of the image based on intensity and contrast. The basic algorithm: (1) For each point on a regular grid in the data base image, find the maximum correlation value for its neighborhood in the input image. This system assumes that the images are already approximately registered, so that the search for the exact matching point is in a limited area. The processing begins on one edge of the image and steps across the image, allowing a linkage between adjacent grid points to determine approximate matches within featureless areas. Match locations are interpolated to find the maximum correlation position with accuracy much better than one pixel. (2) Four grid points forming a square in the data base image map to four points forming a quadrilateral in the input image. The points within the quadrilateral are transformed to fit the input square by interpolation. This basic technique can be refined to find matches along the sides of the quadrilateral. (3) A two-dimensional histogram plotting the image intensity value of an individual pixel in one image versus the value in the second image (assuming that the two images are rectified spatially) should lie along the 45o axis. If the mass of points lie along a different angle, then the intensity values are adjusted. This intensity rectification is applied over local areas of the image rather than globally to account for local, but large-scale variations in intensity. Small anomalies will still appear, but these should correspond to true differences in the two images, and thus to changes in the scene. (4) By subtracting the rectified image from the data base image, changes between the two views are apparent. An analysis of the two-dimensional histogram, as used for the intensity rectification, indicates the type of changes that have occurred (objects added or objects removed). BibRef 7207

Ulstad, M.S.,
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WWW Version. Change Detection, Differencing. This work is similar in scope to the work of Lillestrand, but this paper concentrates more on the deatils of the implementation. Before differencing, a non-linear spatial warp and a match of intensity statistics are computed. This allows for global (or local to a large area) changes in the contrast and intensity in addition to the spatial warping. BibRef 7312

Quam, L.H.,
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Earlier: Stanford AIMemo 44, 1968. Change Detection, Differencing. This work was designed for change detection using multiple views of the surface of Mars. Exact orbit positions were not known, but the approximate position was close enough to limit the possible discrepancy between the two images. The basic techniques are similar to those of the work of Lillestrand. Correlation based matching, but locate feature points in the first image to limit the possibilities. Warp the image based on the matching points for subtraction. Basic algorithm: (1) Find the points in the second image that match points on a grid in the first image using correlation values to determine the match. (2) Globally warp the second image to correspond to the first image. (3) Subtract the two images to indicate changes and find highlight regions. This system allowed extreme differences in the camera orientations which are not allowed by the early CDC work ( See also Techniques for Change Detection. and Allen). BibRef

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Eghbali, H.J.,
K-S Test for Detecting Changes from Landsat Imagery Data,
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Ching, W.S.,
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Lambin, E.F.,
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Bruzzone, L., Serpico, S.B.,
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Bruzzone, L.[Lorenzo], Fernandez-Prieto, D.[Diego],
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Johnson, R.D., Kasischke, E.S.,
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Hame, T., Heiler, I., SanMiguel-Ayanz, J.,
An Unsupervised Change Detection and Recognition System for Forestry,
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Liu, S.C., Fu, C.W., Chang, S.Y.,
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Dai, X., Khorram, S.,
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Chen, M.[Mei], Kanade, T.[Takeo], Pomerleau, D.A.[Dean A.], Rowley, H.A.[Henry A.],
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Aach, T.[Til], Kaup, A.[André],
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Sivan, Z.[Zohar], Malah, D.[David],
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Chen, L.C., Rau, J.Y.,
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Moisan, Y., Bernier, M., Dubois, J.M.M.,
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Stow, D.A.,
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Morisette, J.T., Khorram, S., Mace, T.,
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Houhoulis, P.F.[Paula F.], Michener, W.K.[William K.],
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Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
2-D Points with 2-D Structures, Point Matching .