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Change Detection, Differencing.
This work at Control Data Corp. took two real images as input,
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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
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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
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Match locations are interpolated to find the
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(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
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Ulstad, M.S.,
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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.
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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
(
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ICDAR97(Tu-2B)
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Andra, S.,
Al-Kofahi, O.,
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IP(14), No. 3, March 2005, pp. 294-307.
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0501
Survey, Change Detection.
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Bazi, Y.,
Bruzzone, L.,
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Miller, O.[Ofer],
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Caravaggi, I.,
Giada, S.,
Structural Damage Assessments from Ikonos Data Using Change Detection,
Object-Oriented Segmentation, and Classification Techniques,
PhEngRS(71), No. 7, July 2005, pp. 825-838.
Classical change detection methods, object-oriented image
segmentation, and classification techniques are applied to investigate
effectiveness for identifying post-disaster, structurally damaged
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Chadwick, J.[John],
Dorsch, S.[Stephen],
Glenn, N.[Nancy],
Thackray, G.[Glenn],
Shilling, K.[Karen],
Application of multi-temporal high-resolution imagery and GPS in a
study of the motion of a canyon rim landslide,
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Bovolo, F.,
Bruzzone, L.,
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ICIP05(I: 665-668).
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Koch, M.,
Gumuzzio, J.,
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Colbry, D.[Dirk],
Cherba, D.[David], and
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Pattern Recognition for Classification and Matching of Car Tires,
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Soumekh, M.,
Signal Subspace Change Detection in Averaged Multilook SAR Imagery,
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IEEE DOI Link
0601
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Carincotte, C.,
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Unsupervised Change Detection on SAR Images Using Fuzzy Hidden Markov
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IEEE DOI Link
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Wang, H.Q.[Hong-Qing],
Ellis, E.C.[Erle C.],
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Narayan, U.,
Lakshmi, V.,
Jackson, T.J.,
High-Resolution Change Estimation of Soil Moisture Using L-Band
Radiometer and Radar Observations Made During the SMEX02 Experiments,
GeoRS(44), No. 6, June 2006, pp. 1545-1554.
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Gamba, P.,
Dell'Acqua, F.,
Lisini, G.,
Change Detection of Multitemporal SAR Data in Urban Areas Combining
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GeoRS(44), No. 10, October 2006, pp. 2820-2827.
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Moser, G.,
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Generalized Minimum-Error Thresholding for Unsupervised Change
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GeoRS(44), No. 10, October 2006, pp. 2972-2982.
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Moser, G.,
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Unsupervised Change Detection From Multichannel SAR Data by Markovian
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Moser, G.,
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Conditional Copulas for Change Detection in Heterogeneous Remote
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GeoRS(46), No. 5, May 2008, pp. 1428-1441.
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See also statistical approach to the fusion of spectral and spatio-temporal contextual information for the classification of remote-sensing images, A.
See also Partially Supervised Classification of Remote Sensing Images Through SVM-Based Probability Density Estimation.
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Millward, A.A.[Andrew A.],
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Time-Series Analysis of Medium-Resolution, Multisensor Satellite Data
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PhEngRS(72), No. 6, June 2006, pp. 653-664.
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0610
Methodologies that use standardized principal components analysis applied
to selected bands of imagery to identify and date changes in a landscape
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Gaehler, M.[Monika],
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Automated Techniques for Environmental Monitoring and Change Analyses
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PhEngRS(72), No. 7, July 2006, pp. 835-840.
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0610
The development of automated classification methods for vegetation and
biotope type mapping from the new generation of ultra high-resolution
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Castellana, L.,
d'Addabbo, A.,
Pasquariello, G.,
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Neural networks; Change detection; Remote sensing
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The Regularized Iteratively Reweighted MAD Method for Change Detection
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Change Detection in the 1996-1997 AVHRR Oceans Pathfinder Sea Surface
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Nemmour, H.[Hassiba],
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PandRS(61), No. 2, November 2006, pp. 125-133.
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0703
Change detection; Fuzzy Integral; Combination; Support vector machines;
Attractor dynamics
BibRef
Ghosh, S.,
Bruzzone, L.,
Patra, S.,
Bovolo, F.,
Ghosh, A.,
A Context-Sensitive Technique for Unsupervised Change Detection Based
on Hopfield-Type Neural Networks,
GeoRS(45), No. 3, March 2007, pp. 778-789.
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Gautama, S.[Sidharta],
Bellens, R.[Rik],
de Tre, G.[Guy],
Philips, W.[Wilfried],
Relevance Criteria for Spatial Information Retrieval Using
Error-Tolerant Graph Matching,
GeoRS(45), No. 4, April 2007, pp. 810-817.
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Gautama, S.[Sidharta],
Bellens, R.[Rik],
de Tré, G.[Guy],
d'Haeyer, J.[Johan],
Relevance Criteria for Data Mining Using Error-Tolerant Graph Matching,
IWCIA06(277-290).
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Gautama, S.[Sidharta],
Goeman, W.[Werner],
d'Haeyer, J.[Johan],
On the Design of Reliable Graph Matching Techniques for Change
Detection,
CAIP05(596).
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Sundaresan, A.[Ashok],
Varshney, P.K.[Pramod K.],
Arora, M.K.[Manoj K.],
Robustness of Change Detection Algorithms in the Presence of
Registration Errors,
PhEngRS(73), No. 4, April 2007, pp. 375-384.
WWW Version.
0704
Results from comparing the performance of two change detection algorithms
to determine the presence of registration errors.
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Gruenke, M.A.[Matthew A.],
Brock, N.[Neil],
System and method for searching for changes in surveillance video,
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Zhang, L.[Lu],
Liao, M.S.[Ming-Sheng],
Yang, L.M.[Li-Min],
Lin, H.[Hui],
Remote Sensing Change Detection Based on Canonical Correlation Analysis
and Contextual Bayes Decision,
PhEngRS(73), No. 3, March 2007, pp. 311-318.
WWW Version.
0704
A multi-step statistical analysis approach combining Canonical Correlation
Analysis and Contextual Bayes Decision for change detection using bi-temporal
multispectral remotely sensed images.
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Yackel, J.J.[John J.],
Barber, D.G.[David G.],
Observations of Snow Water Equivalent Change on Landfast First-Year Sea
Ice in Winter Using Synthetic Aperture Radar Data,
GeoRS(45), No. 4, April 2007, pp. 1005-1015.
IEEE DOI Link
0704
BibRef
Rau, J.Y.,
Chen, L.C.,
Liu, J.K.,
Wu, T.H.,
Dynamics Monitoring and Disaster Assessment for Watershed Management
Using Time-Series Satellite Images,
GeoRS(45), No. 6, June 2007, pp. 1641-1649.
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Chatelain, F.,
Tourneret, J.Y.,
Inglada, J.,
Ferrari, A.,
Bivariate Gamma Distributions for Image Registration and Change
Detection,
IP(16), No. 7, July 2007, pp. 1796-1806.
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Chatelain, F.,
Tourneret, J.Y.,
Inglada, J.,
Change Detection in Multisensor SAR Images Using Bivariate Gamma
Distributions,
IP(17), No. 3, March 2008, pp. 249-258.
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Inglada, J.[Jordi],
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A New Statistical Similarity Measure for Change Detection in
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Eismann, M.T.,
Meola, J.,
Hardie, R.C.,
Hyperspectral Change Detection in the Presence of Diurnal and Seasonal
Variations,
GeoRS(46), No. 1, January 2008, pp. 237-249.
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0712
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Eismann, M.T.,
Stocker, A.D.,
Nasrabadi, N.M.,
Automated Hyperspectral Cueing for Civilian Search and Rescue,
PIEEE(97), No. 6, June 2009, pp. 1031-1055.
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0905
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Maeda, T.,
Takano, T.,
Discrimination of Local and Faint Changes From Satellite-Borne
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GeoRS(46), No. 9, September 2008, pp. 2684-2691.
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Slatton, K.C.[K. Clint],
Crawford, M.M.[Melba M.],
Chang, L.D.[Li-Der],
Modeling temporal variations in multipolarized radar scattering from
intertidal coastal wetlands,
PandRS(63), No. 5, September 2008, pp. 559-577.
WWW Version.
0804
Polarization; SAR; LIDAR; Change detection; Coast
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Beeri, O.,
Peled, A.,
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PandRS(64), No. 1, January 2009, pp. 47-54.
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0804
Remote sensing; Real-time; Agricultural monitoring; Quality control
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Omitaomu, O.A.,
Ganguly, A.R.,
Patton, B.W.,
Protopopescu, V.A.,
Anomaly Detection in Radiation Sensor Data With Application to
Transportation Security,
ITS(10), No. 2, June 2009, pp. 324-334.
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Pajares, G.,
Guijarro, M.,
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Pajares, G.[Gonzalo],
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A Neural Network Model for Image Change Detection Based on Fuzzy
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Benedek, C.[Csaba],
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Change Detection in Optical Aerial Images by a Multilayer Conditional
Mixed Markov Model,
GeoRS(47), No. 10, October 2009, pp. 3416-3430.
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0910
BibRef
Earlier:
A Mixed Markov model for change detection in aerial photos with large
time differences,
ICPR08(1-4).
IEEE DOI Link
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Im, J.H.[Jung-Ho],
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Enhancing Binary Change Detection Performance Using A Moving Threshold
Window (MTW) Approach,
PhEngRS(75), No. 8, August 2009, pp. 951-962.
WWW Version.
0910
An automated calibration model using a new concept called the Moving
Threshold Window (MTW) was developed to improve binary change
detection methods based on the traditional Symmetric Threshold Window
(STW) approach.
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Theiler, J.[James],
Subpixel Anomalous Change Detection in Remote Sensing Imagery,
Southwest08(165-168).
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0803
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Tahmoush, D.,
Image Differencing Approaches to Medical Image Classification,
AIPR07(22-27).
IEEE DOI Link
0710
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Becker, N.M.,
Brumby, S.,
David, N.A.,
Irvine, J.M.,
Analysis of multispectral imagery and modeling contaminant transport,
AIPR02(71-77).
IEEE DOI Link
0210
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Ray, N.[Nilanjan],
Saha, B.N.[Baidya Nath],
Zhang, H.[Hong],
Change Detection and Object Segmentation:
A Histogram of Features-Based Energy Minimization Approach,
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0812
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Miezianko, R.[Roland],
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Detecting changes in multilayered orthoimages with spatiotemporal
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A minimum description length principle based method for signal change
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ICPR08(1-4).
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A contrast equalization procedure for change detection algorithms:
Applications to remotely sensed images of urban areas,
ICPR08(1-4).
IEEE DOI Link
0812
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Sezer, O.G.[Osman G.],
Mundy, J.L.[Joseph L.],
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Cooper, D.B.[David B.],
NorMaL: Non-compact Markovian Likelihood for change detection,
ICPR08(1-4).
IEEE DOI Link
0812
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Chen, K.M.[Ke-Ming],
Huo, C.L.[Chun-Lei],
Cheng, J.[Jian],
Zhou, Z.X.[Zhi-Xin],
Lu, H.Q.[Han-Qing],
Change detection based on adaptive Markov Random Fields,
ICPR08(1-4).
IEEE DOI Link
0812
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Li, Z.[Zhi],
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A novel scene change detection algorithm based on the 3D wavelet
transform,
ICIP08(1536-1539).
IEEE DOI Link
0810
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Cifuentes, P.[Patricia],
Malpica, J.A.[José A.],
González-Matesanz, F.J.[Francisco J.],
Change Detection with SPOT-5 and FORMOSAT-2 Imageries,
ISVC08(II: 1186-1195).
Springer DOI Link
0812
BibRef
Faur, D.,
Vaduva, C.,
Gavat, I.,
Datcu, M.,
An information theory based image processing chain for change detection
in Earth Observation,
WSSIP08(129-132).
IEEE DOI Link
0806
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Ozay, N.[Necmiye],
Sznaier, M.[Mario],
Camps, O.I.[Octavia I.],
Sequential sparsification for change detection,
CVPR08(1-6).
IEEE DOI Link
0806
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Singh, M.[Maneesh],
Parameswaran, V.[Vasu],
Ramesh, V.[Visvanathan],
Order consistent change detection via fast statistical significance
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CVPR08(1-8).
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0806
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Patra, S.[Swarnajyoti],
Ghosh, S.[Susmita],
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Semi-supervised Learning with Multilayer Perceptron for Detecting
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PReMI07(161-168).
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0712
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Bruzzone, L.[Lorenzo],
Bovolo, F.[Francesca],
Marchesi, S.[Silvia],
A Multiscale Change Detection Technique Robust to Registration Noise,
PReMI07(77-86).
Springer DOI Link
0712
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Hwang, Y.B.[Young-Bae],
Kim, J.S.[Jun-Sik],
Kweon, I.S.[In So],
Determination of Color Space for Accurate Change Detection,
ICIP06(3021-3024).
0610
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Mandarino, D.,
Change Detection on Comparametrically Related Images,
ICIP06(1073-1076).
0610
IEEE DOI Link
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Ribnick, E.,
Atev, S.,
Masoud, O.,
Papanikolopoulos, N.P.,
Voyles, R.,
Real-Time Detection of Camera Tampering,
AVSBS06(10-10).
IEEE DOI Link
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Tampering based on large differences between old and new frames.
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Sato, J.[Junji],
Takahashi, T.[Tomokazu],
Ide, I.[Ichiro],
Murase, H.[Hiroshi],
Change detection in streetscapes from GPS coordinated omni-directional
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ICPR06(IV: 935-938).
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Li, W.M.[Wei-Ming],
Li, X.M.[Xiao-Ming],
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A Novel Framework for Urban Change Detection Using VHR Satellite Images,
ICPR06(II: 312-315).
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Kita, Y.[Yasuyo],
A study of change detection from satellite images using joint intensity
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ICPR08(1-4).
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Earlier:
Change detection using joint intensity histogram,
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WWW Version.
0609
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Pajares, G.[Gonzalo],
Ruz, J.J.[José Jaime],
de la Cruz, J.M.[Jesús Manuel],
Performance Analysis of Homomorphic Systems for Image Change Detection,
IbPRIA05(I:563).
Springer DOI Link
0509
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Lanza, A.[Alessandro],
di Stefano, L.[Luigi],
Soffritti, L.[Luca],
Bayesian Order-Consistency Testing with Class Priors Derivation for
Robust Change Detection,
AVSBS09(460-465).
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Lanza, A.,
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Bevilacqua, A.[Alessandro],
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An efficient change detection algorithm based on a statistical
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Harasse, S.,
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Automated camera dysfunctions detection,
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Detect changes that indicate the camera is not working.
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Multi-block PCA method for image change detection,
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Detection of major changes in satellite images,
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Angiati, D.,
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Marcenaro, L.,
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Capel, D.,
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Heikkonen, J.,
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Lu, W.,
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Sugano, M.,
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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 .