12.1.5 Change Detection -- Image Level

Chapter Contents (Back)
Change Detection. Some of the error analysis: See also Misregistration Errors, Evaluation Change Detection. See also Change Detection for Damage Assessment. See also Land Cover Change Analysis, Remote Sensing Change Analysis, Temporal Analysis.

Cathey, W.T., Doidge, J.G.,
Image Comparison by Interference,
JOSA(56), August 1966, pp. 1139-1140. BibRef 6608

Allen, G.R., Bonrud, L.O., Cosgrove, J.J., and Stone, R.M.,
The Design and Use of Special Purpose Processors for the Machine Processing of Remotely Sensed Data,
MPRSD73(xx). Introduction to CDC hardware. BibRef 7300

Lillestrand, R.L.,
Techniques for Change Detection,
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.,
An Algorithm for Estimating Small Scale Differences Between Two Digital Images,
PR(5), No. 4, December 1973, pp. 323-330.
WWW Link. 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.,
Computer Comparison of Pictures,
Ph.D.Thesis (CS), May 1971, BibRef 7105 Stanford AIMemo 144. BibRef
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

Marshall, J.M.[James M.], Biglow, J.W.[Jamew W.],
Surveillance System,
US_Patent3,740,466, Jun 1973
WWW Link. Finding changes. BibRef 7306

Bosley, E.J.[Emile J.],
Image Motion and Change Transducers,
US_Patent3,823,261, Jul 1974
WWW Link. BibRef 7407

Chow, C.K., Hilal, S.K., Niehbuhr, K.E.,
X-Ray Image Subtraction by Digital Means,
IBMRD(17), No. 3, May 1973, pp. 206-218. BibRef 7305

Eghbali, H.J.,
K-S Test for Detecting Changes from Landsat Imagery Data,
SMC(9), No. 1, 1979, pp. 17-23. BibRef 7900

Araki, T.[Tsunehiko], Furukawa, S.[Satoshi], Satake, T.[Tadashi], Himezawa, H.[Hidekazu],
Abnormality supervising system,
US_Patent4,737,847, Apr 12, 1988
WWW Link. Changes between images. BibRef 8804

Koezuka, T.[Tetsuo], Tsukahara, H.[Hiroyuki], Nakashima, M.[Masato],
Pattern matching method and apparatus,
US_Patent4,805,224, Feb 14, 1989
WWW Link. BibRef 8902

Guerreri, B.G.[Bart G.],
Image change detection system,
US_Patent4,779,095, Oct 18, 1988
WWW Link. BibRef 8810

Fung, T., and LeDrew, E.,
The determination of optimal thresholds for change detection using various accuracy indices,
PhEngRS(54), 1988, pp. 1449-1454. BibRef 8800

Seto, Y.[Youichi], Komura, F.[Fuminobu],
Method of detecting change using image,
US_Patent4,912,770, Mar 27, 1990
WWW Link. BibRef 9003

Kadar, I.[Ivan],
Method and apparatus for detecting innovations in a scene,
US_Patent4,931,868, Jun 5, 1990
WWW Link. BibRef 9006

Ueda, R.[Ryuichi], Nakamura, M.[Masaaki], Iwasaki, T.[Toshio], Hirota, K.[Kanji], Nakamura, T.[Tetsuya],
Monitoring system using infrared image processing,
US_Patent4,999,614, Mar 12, 1991
WWW Link. BibRef 9103

Ching, W.S.,
A Novel Change Detection Algorithm Using Adaptive Threshold,
IVC(12), No. 7, September 1994, pp. 459-463.
WWW Link. Change Detection, Differencing. BibRef 9409

Cortelazzo, G.M., Deretta, G., Mian, G.A., Zamperoni, P.,
On the Application of Geometrical Form Description Techniques to Automatic Key-Sections Recognition,
PR(26), No. 1, January 1993, pp. 89-94.
WWW Link. 0401
BibRef
Earlier: ICPR92(I:420-424).
IEEE DOI BibRef

Williams, G.L.[Glenn L.],
Video event trigger and tracking system using fuzzy comparators,
US_Patent5,539,454, Jul 23, 1996
WWW Link. BibRef 9607

Markandey, V.[Vishal], Reid, A.[Anthony],
System and method for indicating a change between images,
US_Patent5,500,904, Mar 19, 1996
WWW Link. BibRef 9603

Wong, R.K., Fung, T., Leung, K.S., Leung, Y.,
The Compression of a Sequence of Satellite Images Based on Change Detection,
JRS(18), No. 11, July 20 1997, pp. 2427-2436. 9708
BibRef

Bruzzone, L., Serpico, S.B.,
Detection of Changes in Remotely-Sensed Images by the Selective Use of Multispectral Information,
JRS(18), No. 18, December 1997, pp. 3883-3888. 9801
BibRef

Bruzzone, L.[Lorenzo], Fernandez-Prieto, D.[Diego],
A minimum-cost thresholding technique for unsupervised change detection,
JRS(21), No. 18, December 2000, pp. 3539-3544. 0102
BibRef
Earlier:
An MRF Approach to Unsupervised Change Detection,
ICIP99(I:143-147).
IEEE DOI See also adaptive semiparametric and context-based approach to unsupervised change detection multitemporal remote-sensing images, An. BibRef

Bruzzone, L., Cossu, R.,
An adaptive approach to reducing registration noise effects in unsupervised change detection,
GeoRS(41), No. 11, November 2003, pp. 2455-2465.
IEEE Abstract. 0311
BibRef

Moraleda, J.[Jorge],
Large scalability in document image matching using text retrieval,
PRL(33), No. 7, 1 May 2012, pp. 863-871.
Elsevier DOI 1203
Award, ICPR. Image matching; Image based document retrieval; Document image matching BibRef

Moraleda, J.[Jorge], Hull, J.J.[Jonathan J.],
Toward Massive Scalability in Image Matching,
ICPR10(3424-3427).
IEEE DOI 1008
Blurry document images. BibRef

Liu, S.C., Fu, C.W., Chang, S.Y.,
Statistical Change Detection with Moments Under Time-Varying Illumination,
IP(7), No. 9, September 1998, pp. 1258-1268.
IEEE DOI 9809
BibRef

Aach, T.[Til], Kaup, A.[André],
Bayesian algorithms for adaptive change detection in image sequences using Markov random fields,
SP:IC(7), No. 2, August 1995, pp. 147-160.
WWW Link. BibRef 9508

Sivan, Z.[Zohar], Malah, D.[David],
Change detection and texture analysis for image sequence coding,
SP:IC(6), No. 4, August 1994, pp. 357-376.
WWW Link. BibRef 9408

Igbokwe, J.I.,
Geometrical processing of multi-sensoral multi-temporal satellite images for change detection studies,
JRS(20), No. 6, April 1999, pp. 1141. BibRef 9904

Moisan, Y., Bernier, M., Dubois, J.M.M.,
Detection des changements dans une serie d'images ERS-1 multidates a l'aide de l'analyse en composantes principales,
JRS(20), No. 6, April 1999, pp. 1149. BibRef 9904

Doi, K.[Kunio], Ishida, T.[Takayuki], Katsuragawa, S.[Shigehiko],
Method of detecting interval changes in chest radiographs utilizing temporal subtraction combined with automated initial matching of blurred low resolution images,
US_Patent5,982,915, Nov 9, 1999
WWW Link. BibRef 9911

Courtney, J.D.[Jonathan D.], Nair, D.R.[Dinesh R.],
Object detection method and system for scene change analysis in TV and IR data,
US_Patent6,049,363, Apr 11, 2000
WWW Link. BibRef 0004

Yang, X.J.[Xiao-Jun], Lo, C.P.,
Relative Radiometric Normalization Performance for Change Detection from Multi-Date Satellite Images,
PhEngRS(66), No. 8, August 2000, pp. 967-980. 0008
BibRef

Morisette, J.T.[Jeffrey T.], Khorram, S.[Siamak],
Accuracy Assessment Curves for Satellite-Based Change Detection,
PhEngRS(66), No. 7, July 2000, pp. 875-880. A graphical technique to assess change-detection accuracy assessment figures and how this supports the benefits of a continuous satellite-based change-detection product is explored. 0008
BibRef

Smits, P.C., Annoni, A.,
Toward Specification-Driven Change Detection,
GeoRS(38), No. 3, May 2000, pp. 1484-1488.
IEEE Top Reference. 0006
BibRef

Song, C.H.[Cong-He], Woodcock, C.E.[Curtis E.], Seto, K.C.[Karen C.], Lenney, M.P.[Mary Pax], Macomber, S.A.[Scott A.],
Classification and Change Detection Using Landsat TM Data. When and How to Correct Atmospheric Effects?,
RSE(75), No. 2, 2001, pp. 230- 244. 0102
BibRef

Li, L.Y.[Li-Yuan], Leung, M.K.H.,
Integrating intensity and texture differences for robust change detection,
IP(11), No. 2, February 2002, pp. 105-112.
IEEE DOI 0202
BibRef
Earlier:
Robust Change Detection by Fusing Intensity and Texture Differences,
CVPR01(I:777-784).
IEEE DOI 0110
Both intensity and texture. BibRef

Bromiley, P.A., Thacker, N.A., Courtney, P.,
Non-Parametric Image Subtraction using Grey Level Scattergrams,
IVC(20), No. 9-10, August 2002, pp. 609-617.
WWW Link. 0208
BibRef
Earlier: BMVC00(xx-yy).
PDF File. 0009
BibRef

Luo, J.B.[Jie-Bo], Etz, S.P.[Stephen P.], Gray, R.T.[Robert T.],
Normalized Kemeny and Snell Distance: A Novel Metric for Quantitative Evaluation of Rank-Order Similarity of Images,
PAMI(24), No. 8, August 2002, pp. 1147-1151.
IEEE Abstract. 0208
BibRef
Earlier: A2, A1, A3:
Quantitative Evaluation of Rank-order Similarity of Images,
ICIP00(Vol I: 485-488).
IEEE DOI 0008
Main subject detection (region of interest for indexing). BibRef

Shishido, C.[Chie], Hiroi, T.[Takashi], Yoda, H.[Haruo], Watanabe, M.[Masahiro], Kuni, A.[Asahiro], Tanaka, M.[Maki], Ninomiya, T.[Takanori], Doi, H.[Hideaki], Maeda, S.J.[Shun-Ji], Nozoe, M.[Mari], Shinoda, H.[Hiroyuki], Takafuji, A.[Atsuko], Sugimoto, A.[Aritoshi], Usami, Y.[Yasutsugu],
Method of inspecting pattern and apparatus thereof with a differential brightness image detection,
US_Patent6,236,057, May 22, 2001
WWW Link. BibRef 0105

Kasetkasem, T., Varshney, P.K.,
An image change detection algorithm based on markov random field models,
GeoRS(40), No. 8, August 2002, pp. 1815-1823.
IEEE Top Reference. 0210
BibRef

Rosin, P.L.[Paul L.],
Thresholding for Change Detection,
CVIU(86), No. 2, May 2002, pp. 79-95.
WWW Link. 0301
BibRef
Earlier: ICCV98(274-279).
IEEE DOI
PDF File. BibRef
Earlier: BMVC97(212-221).
HTML Version. See also Unimodal Thresholding. BibRef

Rosin, P.L.[Paul L.], Ioannidis, E.[Efstathios],
Evaluation of global image thresholding for change detection,
PRL(24), No. 14, October 2003, pp. 2345-2356.
WWW Link.
PDF File. 0307
BibRef

Vokhmin, P.A.[Peter A.],
Method and system for automatic non-contact measurements of optical properties of optical objects,
US_Patent6,496,253, Dec 17, 2002
WWW Link. Compare to test pattern BibRef 0212

Silver, W.[William], Walleck, A.[Aaron], Wagman, A.[Adam],
Fast high-accuracy multi-dimensional pattern inspection,
US_Patent6,836,567, Dec 28, 2004
WWW Link. BibRef 0412

Xie, B.L.[Bing-Long], Ramesh, V.[Visvanathan], Boult, T.E.[Terrance E.],
Sudden illumination change detection using order consistency,
IVC(22), No. 2, 1 February 2004, pp. 117-125.
WWW Link. 0402
Using graphics model, change detection with large illumination changes. BibRef

Parameswaran, V.[Vasu], Singh, M.[Maneesh], Ramesh, V.[Visvanathan],
Illumination compensation based change detection using order consistency,
CVPR10(1982-1989).
IEEE DOI 1006
BibRef

Miller, O.[Ofer], Pikaz, A.[Arie], Averbuch, A.[Amir],
Objects based change detection in a pair of gray-level images,
PR(38), No. 11, November 2005, pp. 1976-1992.
WWW Link. 0509
BibRef

Colbry, D.[Dirk], Cherba, D.[David], and Luchini, J.[John],
Pattern Recognition for Classification and Matching of Car Tires,
Other JournalJournal of Tire Science and Technology, Vol. 33, No. 1, 2005, pp. 2-17. 0906
BibRef

Liu, Q.A.[Qi-Ang], Sclabassi, R.J.[Robert J.], Li, C.C.[Ching-Chung], Sun, M.G.[Min-Gui],
An Application of MAP-MRF to Change Detection in Image Sequence Based on Mean Field Theory,
JASP(2005), No. 13, 2005, pp. 1956-1968.
WWW Link. 0603
BibRef

Nemmour, H.[Hassiba], Chibani, Y.[Youcef],
Neural Network Combination by Fuzzy Integral for Robust Change Detection in Remotely Sensed Imagery,
JASP(2005), No. 14, 2005, pp. 2187-2195.
WWW Link. 0603
BibRef

Lee, H.C.[Harry C.], Sefcik, J.[Jason],
Method and apparatus for image processing using sub-pixel differencing,
US_Patent6,961,481, Nov 1, 2005
WWW Link. BibRef 0511

Bourennane, S., Marot, J.,
Estimation of straight line offsets by high-resolution method,
VISP(153), No. 2, April 2006, pp. 224-229.
DOI Link 0604
BibRef

Mercier, G., Moser, G., Serpico, S.B.[Sebastiano B.],
Conditional Copulas for Change Detection in Heterogeneous Remote Sensing Images,
GeoRS(46), No. 5, May 2008, pp. 1428-1441.
IEEE DOI 0804
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. BibRef

Liu, Z., Dezert, J., Mercier, G., Pan, Q.,
Dynamic Evidential Reasoning for Change Detection in Remote Sensing Images,
GeoRS(50), No. 5, May 2012, pp. 1955-1967.
IEEE DOI 1202
BibRef

Millward, A.A.[Andrew A.], Piwowar, J.M.[Joseph M.], Howarth, P.J.[Philip J.],
Time-Series Analysis of Medium-Resolution, Multisensor Satellite Data for Identifying Landscape Change,
PhEngRS(72), No. 6, June 2006, pp. 653-664.
WWW Link. 0610
Methodologies that use standardized principal components analysis applied to selected bands of imagery to identify and date changes in a landscape across a time series of multisensor imagery. BibRef

Ehlers, M.[Manfred], Gaehler, M.[Monika], Janowsky, R.[Ronald],
Automated Techniques for Environmental Monitoring and Change Analyses for Ultra High-resolution Remote Sensing Data,
PhEngRS(72), No. 7, July 2006, pp. 835-840.
WWW Link. 0610
The development of automated classification methods for vegetation and biotope type mapping from the new generation of ultra high-resolution remote sensing data. BibRef

Castellana, L., d'Addabbo, A., Pasquariello, G.,
A composed supervised/unsupervised approach to improve change detection from remote sensing,
PRL(28), No. 4, 1 March 2007, pp. 405-413.
WWW Link. 0701
Neural networks; Change detection; Remote sensing BibRef

Nielsen, A.A.,
The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data,
IP(16), No. 2, February 2007, pp. 463-478.
IEEE DOI 0702
BibRef

Nielsen, A.A., Conradsen, K., Andersen, O.B.,
Change Detection in the 1996-1997 AVHRR Oceans Pathfinder Sea Surface Temperature Data,
SCIA01(O-Tu4A). 0206
BibRef

Ji, L.[Lei], Gallo, K.[Kevin],
An Agreement Coefficient for Image Comparison,
PhEngRS(72), No. 7, July 2006, pp. 823-834.
WWW Link. 0610
A non-dimensional measure of agreement was developed to evaluate the agreement between two different images. 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.
IEEE DOI 0703
BibRef

Ghosh, A., Subudhi, B.N., Bruzzone, L.,
Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images,
IP(22), No. 8, 2013, pp. 3087-3096.
IEEE DOI 1307
Hopfield neural nets; Markov processes; Gibbs Markov random field integration; graph-cut algorithm; Change detection See also Entropy based region selection for moving object detection. BibRef

Subudhi, B.N.[Badri Narayan], Ghosh, S.[Susmita], Ghosh, A.[Ashish],
Spatial constraint Hopfield-type neural networks for detecting changes in remotely sensed multitemporal images,
ICIP13(3815-3819)
IEEE DOI 1402
BibRef

Marchesi, S.[Silvia], Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
A Context-Sensitive Technique Robust to Registration Noise for Change Detection in VHR Multispectral Images,
IP(19), No. 7, July 2010, pp. 1877-1889.
IEEE DOI 1007
BibRef
Earlier: A3, A2, A1:
A Multiscale Change Detection Technique Robust to Registration Noise,
PReMI07(77-86).
Springer DOI 0712
BibRef

Bovolo, F.[Francesca], Camps-Valls, G., Bruzzone, L.[Lorenzo],
A support vector domain method for change detection in multitemporal images,
PRL(31), No. 10, 15 July 2010, pp. 1148-1154.
Elsevier DOI 1008
Unsupervised change detection; Support vector domain description; Kernel methods; Bayesian thresholding; Change vector analysis; Remote sensing BibRef

Han, Y.[Youkyung], Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
Fine co-registration of VHR images for multitemporal Urban area analysis,
MultiTemp15(1-4)
IEEE DOI 1511
feature extraction BibRef

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.
IEEE DOI 0704
BibRef

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).
Springer DOI 0606
BibRef

Gautama, S.[Sidharta], Goeman, W.[Werner], d'Haeyer, J.[Johan],
On the Design of Reliable Graph Matching Techniques for Change Detection,
CAIP05(596).
Springer DOI 0509
BibRef

Buehler, C.J.[Christopher J.], Gruenke, M.A.[Matthew A.], Brock, N.[Neil],
System and method for searching for changes in surveillance video,
US_Patent7,280,673, Oct 9, 2007
WWW Link. BibRef 0710

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 Link. 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. 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.
IEEE DOI 0706
BibRef

Kawada, R.[Ryoichi], Sugimoto, O.[Osamu], Wada, M.[Masahiro], Koike, A.[Atsushi],
Image matching device and method for motion pictures,
US_Patent7,305,032, Dec 4, 2007
WWW Link. BibRef 0712

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.
IEEE DOI 0712
BibRef

Meola, J., Eismann, M.T., Moses, R.L., Ash, J.N.,
Detecting Changes in Hyperspectral Imagery Using a Model-Based Approach,
GeoRS(49), No. 7, July 2011, pp. 2647-2661.
IEEE DOI 1107
BibRef

Meola, J., Eismann, M.T., Moses, R.L., Ash, J.N.,
Application of Model-Based Change Detection to Airborne VNIR/SWIR Hyperspectral Imagery,
GeoRS(50), No. 10, October 2012, pp. 3693-3706.
IEEE DOI 1210
BibRef

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.
IEEE DOI 0905
BibRef

Maeda, T., Takano, T.,
Discrimination of Local and Faint Changes From Satellite-Borne Microwave-Radiometer Data,
GeoRS(46), No. 9, September 2008, pp. 2684-2691.
IEEE DOI 0810
BibRef

Maeda, T., Takano, T.,
Detection Algorithm of Earthquake-Related Rock Failures From Satellite-Borne Microwave Radiometer Data,
GeoRS(48), No. 4, April 2010, pp. 1768-1776.
IEEE DOI 1003
BibRef

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.
IEEE DOI 0906
BibRef

Pajares, G., Guijarro, M., Herrera, P.J., Ribeiro, A.,
Combining classifiers through fuzzy cognitive maps in natural images,
IET-CV(3), No. 3, September 2009, pp. 112-123.
DOI Link 0909
BibRef

Guijarro, M., Fuentes-Fernandez, R., Herrera, P.J., Ribeiro, A., Pajares, G.,
New unsupervised hybrid classifier based on the fuzzy integral: applied to natural textured images,
IET-CV(7), No. 4, 2013, pp. -.
DOI Link 1307
BibRef

Pajares, G.[Gonzalo], Sánchez-Beato, A.[Alfonso], Cruz, J.M.[Jesús M.], Ruz, J.J.[José J.],
A Neural Network Model for Image Change Detection Based on Fuzzy Cognitive Maps,
IbPRIA07(I: 595-602).
Springer DOI 0706
BibRef

Benedek, C.[Csaba], Sziranyi, T.[Tamas],
Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model,
GeoRS(47), No. 10, October 2009, pp. 3416-3430.
IEEE DOI 0910
BibRef
Earlier:
A Mixed Markov model for change detection in aerial photos with large time differences,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Im, J.H.[Jung-Ho], Rhee, J.Y.[Jin-Young], Jensen, J.R.[John R.],
Enhancing Binary Change Detection Performance Using A Moving Threshold Window (MTW) Approach,
PhEngRS(75), No. 8, August 2009, pp. 951-962.
WWW Link. 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. BibRef

Carrao, H., Gonsalves, P., Caetano, M.,
A Nonlinear Harmonic Model for Fitting Satellite Image Time Series: Analysis and Prediction of Land Cover Dynamics,
GeoRS(48), No. 4, April 2010, pp. 1919-1930.
IEEE DOI 1003
BibRef

Celik, T.[Turgay], Ma, K.K.[Kai-Kuang],
Unsupervised Change Detection for Satellite Images Using Dual-Tree Complex Wavelet Transform,
GeoRS(48), No. 3, March 2010, pp. 1199-1210.
IEEE DOI 1003
BibRef

Celik, T.[Turgay], Ma, K.K.[Kai-Kuang],
Multitemporal Image Change Detection Using Undecimated Discrete Wavelet Transform and Active Contours,
GeoRS(49), No. 2, February 2011, pp. 706-716.
IEEE DOI 1102
BibRef

Celik, T.[Turgay],
Image Change Detection Using Gaussian Mixture Model and Genetic Algorithm,
JVCIR(21), No. 8, November 2010, pp. 965-974.
Elsevier DOI 1011
Gaussian mixture model; Genetic algorithm; Parameter estimation; Bayesian inference; Change detection; Difference image; Log-ratio image; Remote sensing; Optical image; Advanced synthetic aperture radar See also Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modeling. BibRef

Celik, T.[Turgay],
Bayesian change detection based on spatial sampling and Gaussian mixture model,
PRL(32), No. 12, 1 September 2011, pp. 1635-1642.
Elsevier DOI 1108
Change detection; Difference image; Log-ratio image; Gaussian mixture model; Bayesian inferencing; Binary thresholding BibRef

Bazi, Y., Melgani, F., Al-Sharari, H.D.,
Unsupervised Change Detection in Multispectral Remotely Sensed Imagery With Level Set Methods,
GeoRS(48), No. 8, August 2010, pp. 3178-3187.
IEEE DOI 1008
BibRef

Ma, L., Zheng, N., Yuan, Z., Zhang, X.,
A Novel Dual-Probe Adaptive Model for Image Change Detection,
SPLetters(17), No. 10, October 2010, pp. 863-866.
IEEE DOI 1008
BibRef

Yokoi, K.[Kentaro],
Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements,
IEICE(E93-D), No. 7, July 2010, pp. 1700-1707.
WWW Link. 1008
BibRef
Earlier: MVA09(148-).
PDF File. 0905
BibRef

Mahecha, M.D.[Miguel D.], Furst, L.M.[Lina M.], Gobron, N.[Nadine], Lange, H.[Holger],
Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity,
PRL(31), No. 14, 15 October 2010, pp. 2309-2317.
Elsevier DOI 1003
Spatiotemporal data; Nonlinear dimensionality reduction; Isomap; Time series analysis; Singular spectrum analysis; FAPAR BibRef

Robin, A.[Amandine], Moisan, L.[Lionel], le Hegarat-Mascle, S.[Sylvie],
An a-Contrario Approach for Subpixel Change Detection in Satellite Imagery,
PAMI(32), No. 11, November 2010, pp. 1977-1993.
IEEE DOI 1011
probabilistic model of level of coherence in image series. BibRef

Chang, N.B.[Ni-Bin],
Satellite-based multitemporal-change detection in urban environments,
SPIE(Newsroom), February 23, 2011.
DOI Link 1102
High-resolution optical satellite sensors can contribute to improved coastal and land management, hazard mitigation, emergency response, and ecosystem-service design. BibRef

Flenner, A.[Arjuna], Hewer, G.[Gary],
A Helmholtz Principle Approach To Parameter Free Change Detection And Coherent Motion Using Exchangeable Random Variables,
SIIMS(4), No. 1, 2011, pp. 243-276.
DOI Link 1106
Helmholtz principle; hypergeometric distribution; change detection; coherent motion; large deviation theory BibRef

Weiss, P.[Pierre], Fournier, A.[Alexandre], Blanc-Feraud, L.[Laure], Aubert, G.[Gilles],
On The Illumination Invariance Of The Level Lines Under Directed Light: Application To Change Detection,
SIIMS(4), No. 1, 2011, pp. 448-471.
DOI Link 1106
level lines; topographic map; illumination invariance; contrast equalization; change detection; remote sensing BibRef

Lanza, A.[Alessandro], di Stefano, L.[Luigi],
Statistical Change Detection by the Pool Adjacent Violators Algorithm,
PAMI(33), No. 9, September 2011, pp. 1894-1910.
IEEE DOI 1109
BibRef
Earlier:
Detecting Changes in Grey Level Sequences by ML Isotonic Regression,
AVSBS06(4-4).
IEEE DOI 0611
Robust to changes in illumination, camera parameters. Model these changes, look for changes that do not fit. BibRef

Bevilacqua, A.[Alessandro], di Stefano, L.[Luigi], Lanza, A.[Alessandro],
An efficient change detection algorithm based on a statistical non-parametric camera noise model,
ICIP04(IV: 2347-2350).
IEEE DOI 0505
BibRef

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).
IEEE DOI 0909
BibRef

Salti, S.[Samuele], Lanza, A.[Alessandro], di Stefano, L.[Luigi],
Bayesian Loop for Synergistic Change Detection and Tracking,
VS10(43-53).
Springer DOI 1109
See also Non-linear parametric Bayesian regression for robust background subtraction. BibRef

Lanza, A., di Stefano, L., Berclaz, J., Fleuret, F., Fua, P.,
Robust Multi-View Change Detection,
BMVC07(xx-yy).
PDF File. 0709
BibRef

Gueguen, L., Soille, P., Pesaresi, M.,
Change Detection Based on Information Measure,
GeoRS(49), No. 11, November 2011, pp. 4503-4515.
IEEE DOI 1112
BibRef

Alberga, V.,
Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications,
RS(1), No. 3, September 2009, pp. 122-143.
DOI Link 1203
BibRef

Gao, Z., Gao, W., Chang, N.,
Detection of Multidecadal Changes in UVB and Total Ozone Concentrations over the Continental US with NASA TOMS Data and USDA Ground-Based Measurements,
RS(2), No. 1, January 2010, pp. 262-277.
DOI Link 1203
BibRef

Almutairi, A., Warner, T.,
Change Detection Accuracy and Image Properties: A Study Using Simulated Data,
RS(2), No. 6, June 2010, pp. 1508-1529.
DOI Link 1203
BibRef

de Carvalho Júnior, O.A., Guimarăes, R.F., Gillespie, A.R., Silva, N.C., Gomes, R.A.T.,
A New Approach to Change Vector Analysis Using Distance and Similarity Measures,
RS(3), No. 11, November 2011, pp. 2473-2493.
DOI Link 1203
See also Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset. BibRef

Theiler, J., Wohlberg, B.,
Local Coregistration Adjustment for Anomalous Change Detection,
GeoRS(50), No. 8, August 2012, pp. 3107-3116.
IEEE DOI 1208
BibRef

Ilsever, M.[Murat], Ünsalan, C.[Cem],
Two-Dimensional Change Detection Methods,
Springer2012. ISBN 978-1-4471-4254-6


WWW Link. 1208
BibRef

de Kok, R.,
Spectral Difference in the Image Domain for Large Neighborhoods, a GEOBIA Pre-Processing Step for High Resolution Imagery,
RS(4), No. 8, August 2012, pp. 2294-2313.
DOI Link 1209
BibRef

David, S., Visvikis, D., Quellec, G., Cheze Le Rest, C., Fernandez, P., Allard, M., Roux, C., Hatt, M.,
Image Change Detection Using Paradoxical Theory for Patient Follow-Up Quantitation and Therapy Assessment,
MedImg(31), No. 9, September 2012, pp. 1743-1753.
IEEE DOI 1209
BibRef

Oskouei, M.M.[Majid M.],
Independent Component Analysis of Hyperion Data to Map Alteration Zones,
PFG(2010), No. 3, 2010, pp. 179-189.
WWW Link. 1211
BibRef

Belghith, A.[Akram], Collet, C.[Christophe], Armspach, J.P.[Jean Paul],
Change detection based on a support vector data description that treats dependency,
PRL(34), No. 3, 1 February 2013, pp. 275-282.
Elsevier DOI 1301
BibRef
Change-detection based on support vector data description handling dependency,
ICIP11(2905-2908).
IEEE DOI 1201
Classification; SVDD; Change detection; Copula theory BibRef

Subudhi, B.N.[Badri Narayan], Ghosh, S.[Susmita], Ghosh, A.[Ashish],
Change detection for moving object segmentation with robust background construction under Wronskian framework,
MVA(24), No. 4, May 2013, pp. 795-809.
WWW Link. 1304
See also Entropy based region selection for moving object detection. BibRef

Patra, S.[Swarnajyoti], Ghosh, S.[Susmita], Ghosh, A.[Ashish],
Semi-supervised Learning with Multilayer Perceptron for Detecting Changes of Remote Sensing Images,
PReMI07(161-168).
Springer DOI 0712
BibRef

de Morsier, F., Tuia, D., Borgeaud, M., Gass, V., Thiran, J.P.,
Semi-Supervised Novelty Detection Using SVM Entire Solution Path,
GeoRS(51), No. 4, April 2013, pp. 1939-1950.
IEEE DOI 1304
BibRef

Jin, J.H.[Jung-Hwan], Shin, H.J.[Hyun Joon], Choi, J.J.[Jung-Ju],
SPOID: A system to produce spot-the-difference puzzle images with difficulty,
VC(29), No. 6-8, June 2013, pp. 481-489.
WWW Link. 1306
BibRef

Dias, Z.[Zanoni], Goldenstein, S.K.[Siome K.], Rocha, A.[Anderson],
Exploring heuristic and optimum branching algorithms for image phylogeny,
JVCIR(24), No. 7, 2013, pp. 1124-1134.
Elsevier DOI 1309
Image phylogeny tree BibRef

Kujawinska, M.[Malgorzata], Malesa, M.[Marcin], Malowany, K.[Krzysztof],
Measuring structural displacements with digital image correlation,
SPIE(Newsroom), September 18 2013.
DOI Link 1310
A new technique that automatically merges temporally distributed data is used to monitor changes both in power station pipelines and art conservation. BibRef

Csapo, I.[Istvan], Davis, B.[Brad], Shi, Y.[Yundi], Sanchez, M.[Mar], Styner, M.[Martin], Niethammer, M.[Marc],
Longitudinal Image Registration With Temporally-Dependent Image Similarity Measure,
MedImg(32), No. 10, 2013, pp. 1939-1951.
IEEE DOI 1311
BibRef
Earlier:
Temporally-Dependent Image Similarity Measure for Longitudinal Analysis,
WBIR12(99-109).
Springer DOI 1208
biomedical MRI. Temporal registrations. BibRef

Csapo, I.[Istvan], Shi, Y.[Yundi], Sanchez, M.[Mar], Styner, M.[Martin], Niethammer, M.[Marc],
Registration of Developmental Image Sequences with Missing Data,
WBIR16(558-565)
IEEE DOI 1612
longitudinal images of brain development. BibRef

Wu, C.[Chen], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Slow Feature Analysis for Change Detection in Multispectral Imagery,
GeoRS(52), No. 5, May 2014, pp. 2858-2874.
IEEE DOI 1403
Change detection;image transformation;slow feature analysis (SFA) BibRef

Tang, Y.[Yuqi], Zhang, L.P.[Liang-Pei],
Urban Change Analysis with Multi-Sensor Multispectral Imagery,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Zhang, L., Wu, C., Du, B.,
Automatic Radiometric Normalization for Multitemporal Remote Sensing Imagery With Iterative Slow Feature Analysis,
GeoRS(52), No. 10, October 2014, pp. 6141-6155.
IEEE DOI 1407
Covariance matrices BibRef

Wu, C., Zhang, L., Du, B.,
Kernel Slow Feature Analysis for Scene Change Detection,
GeoRS(55), No. 4, April 2017, pp. 2367-2384.
IEEE DOI 1704
Bayes methods BibRef

Brooks, E.B., Wynne, R.H., Thomas, V.A., Blinn, C.E., Coulston, J.W.,
On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data,
GeoRS(52), No. 6, June 2014, pp. 3316-3332.
IEEE DOI 1403
Control charts BibRef

Lingg, A.J., Zelnio, E., Garber, F., Rigling, B.D.,
A Sequential Framework for Image Change Detection,
IP(23), No. 5, May 2014, pp. 2405-2413.
IEEE DOI 1405
Computational modeling BibRef

Hernandez-Lopez, F.J.[Francisco J.], Rivera, M.[Mariano],
Change detection by probabilistic segmentation from monocular view,
MVA(25), No. 5, July 2014, pp. 1175-1195.
WWW Link. 1407
BibRef

Atia, G.K.,
Change Detection with Compressive Measurements,
SPLetters(22), No. 2, February 2015, pp. 182-186.
IEEE DOI 1410
Gaussian processes BibRef

Goyette, N., Jodoin, P.M., Porikli, F.M., Konrad, J., Ishwar, P.,
A Novel Video Dataset for Change Detection Benchmarking,
IP(23), No. 11, November 2014, pp. 4663-4679.
IEEE DOI 1410
Dataset, Change Detection. Adaptive optics BibRef

Wang, Y.[Yi], Jodoin, P.M.[Pierre-Marc], Porikli, F.M.[Fatih M.], Konrad, J.[Janusz], Benezeth, Y.[Yannick], Ishwar, P.[Prakash],
CDnet 2014: An Expanded Change Detection Benchmark Dataset,
CDW14(393-400)
IEEE DOI 1409
Dataset, Change Detection. BibRef

Bouchaffra, D.[Djamel], Cheriet, M.[Mohamed], Jodoin, P.M.[Pierre-Marc], Beck, D.[Diane],
Machine learning and pattern recognition models in change detection,
PR(48), No. 3, 2015, pp. 613-615.
Elsevier DOI 1412
BibRef

Huerta, I.[Ivan], Pedersoli, M.[Marco], Gonzŕlez, J.[Jordi], Sanfeliu, A.[Albert],
Combining where and what in change detection for unsupervised foreground learning in surveillance,
PR(48), No. 3, 2015, pp. 709-719.
Elsevier DOI 1412
Object detection BibRef

Klaric, M.[Matthew],
Predicting Relevant Change in High Resolution Satellite Imagery,
IJGI(3), No. 4, 2014, pp. 1491-1511.
DOI Link 1412
BibRef

St-Charles, P.L.[Pierre-Luc], Bilodeau, G.A.[Guillaume-Alexandre], Bergevin, R.[Robert],
SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity,
IP(24), No. 1, January 2015, pp. 359-373.
IEEE DOI 1502
BibRef
And:
A Self-Adjusting Approach to Change Detection Based on Background Word Consensus,
WACV15(990-997)
IEEE DOI 1503
image colour analysis. Adaptation models See also Universal Background Subtraction Using Word Consensus Models. BibRef

St-Charles, P.L.[Pierre-Luc], Bilodeau, G.A.[Guillaume-Alexandre], Bergevin, R.[Robert],
Online multimodal video registration based on shape matching,
PBVS15(26-34)
IEEE DOI 1510
Context BibRef

Prendes, J.[Jorge], Chabert, M.[Marie], Pascal, F.[Frédéric], Giros, A.[Alain], Tourneret, J.Y.[Jean-Yves],
A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors,
IP(24), No. 3, March 2015, pp. 799-812.
IEEE DOI 1502
geophysical image processing BibRef

Prendes, J.[Jorge], Chabert, M.[Marie], Pascal, F.[Frédéric], Giros, A.[Alain], Tourneret, J.Y.[Jean-Yves],
A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images,
SIIMS(9), No. 4, 2016, pp. 1889-1921.
DOI Link 1612
BibRef

Liu, S.C.[Si-Cong], Bruzzone, L., Bovolo, F., Zanetti, M., Du, P.J.[Pei-Jun],
Sequential Spectral Change Vector Analysis for Iteratively Discovering and Detecting Multiple Changes in Hyperspectral Images,
GeoRS(53), No. 8, August 2015, pp. 4363-4378.
IEEE DOI 1506
geophysical image processing BibRef

Zanetti, M., Bovolo, F., Bruzzone, L.,
Rayleigh-Rice Mixture Parameter Estimation via EM Algorithm for Change Detection in Multispectral Images,
IP(24), No. 12, December 2015, pp. 5004-5016.
IEEE DOI 1512
Gaussian distribution BibRef

Zanetti, M., Bruzzone, L.,
Piecewise Linear Approximation of Vector-Valued Images and Curves via Second-Order Variational Model,
IP(26), No. 9, September 2017, pp. 4414-4429.
IEEE DOI 1708
approximation theory, gradient methods, image colour analysis, image restoration, minimisation, vectors, BZ model, Blake-Zisserman model, RGB imagery, bandwise processing, first-order model, free gradient discontinuity, image restoration-regularization problem, minimization algorithm, multiband imaging, multispectral imagery, piecewise linear approximation, second-order functional model, second-order variational model, vector-valued input imaging, Approximation algorithms, Image edge detection, Image restoration, Mathematical model, Minimization, Numerical models, Piecewise linear approximation, Blake-Zisserman, Multiband image, Mumford-Shah, block-coordinate descent method, piecewise linear approximation, variational, methods BibRef

Liu, S.C.[Si-Cong], Bruzzone, L., Bovolo, F., Du, P.J.[Pei-Jun],
Unsupervised Multitemporal Spectral Unmixing for Detecting Multiple Changes in Hyperspectral Images,
GeoRS(54), No. 5, May 2016, pp. 2733-2748.
IEEE DOI 1604
hyperspectral imaging BibRef

Li, Y., Gong, M., Jiao, L., Li, L., Stolkin, R.,
Change-Detection Map Learning Using Matching Pursuit,
GeoRS(53), No. 8, August 2015, pp. 4712-4723.
IEEE DOI 1506
Dictionaries BibRef

Bosch, I., Serrano, A., Vergara, L., Miralles, R.,
Change detection with texture segmentation and nonlinear filtering in optical remote sensing images,
SIViP(9), No. 8, November 2015, pp. 1955-1963.
WWW Link. 1511
BibRef

Mandanici, E.[Emanuele], Bitelli, G.[Gabriele],
Multi-Image and Multi-Sensor Change Detection for Long-Term Monitoring of Arid Environments With Landsat Series,
RS(7), No. 10, 2015, pp. 14019.
DOI Link 1511
BibRef

Shah-Hosseini, R.[Reza], Homayouni, S.[Saeid], Safari, A.[Abdolreza],
A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space,
RS(7), No. 10, 2015, pp. 12829.
DOI Link 1511
BibRef

Seemakurthy, K.[Karthik], Rajagopalan, A.N.,
Change detection in underwater imagery,
JOSA-A(33), No. 3, March 2016, pp. 301-313.
DOI Link 1603
Water BibRef

Ye, S.[Su], Chen, D.M.[Dong-Mei], Yu, J.[Jie],
A targeted change-detection procedure by combining change vector analysis and post-classification approach,
PandRS(114), No. 1, 2016, pp. 115-124.
Elsevier DOI 1604
Change detection BibRef

Shao, P.[Pan], Shi, W.Z.[Wen-Zhong], He, P.F.[Peng-Fei], Hao, M.[Ming], Zhang, X.K.[Xiao-Kang],
Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm,
RS(8), No. 3, 2016, pp. 264.
DOI Link 1604
BibRef

Shimada, A.[Atsushi], Nagahara, H.[Hajime], Taniguchi, R.I.[Rin-Ichiro],
Background light ray modeling for change detection,
JVCIR(38), No. 1, 2016, pp. 55-64.
Elsevier DOI 1605
BibRef
Earlier:
Change detection on light field for active video surveillance,
AVSS15(1-6)
IEEE DOI 1511
Change detection. image sequences See also Case-based background modeling: associative background database towards low-cost and high-performance change detection. BibRef

Ajadi, O.A.[Olaniyi A.], Meyer, F.J.[Franz J.], Webley, P.W.[Peter W.],
Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach,
RS(8), No. 6, 2016, pp. 482.
DOI Link 1608
BibRef

Krylov, V.A.[Vladimir A.], Moser, G.[Gabriele], Serpico, S.B.[Sebastiano B.], Zerubia, J.B.[Josiane B.],
False Discovery Rate Approach to Unsupervised Image Change Detection,
IP(25), No. 10, October 2016, pp. 4704-4718.
IEEE DOI 1610
BibRef
Earlier:
False discovery rate approach to image change detection,
ICIP13(3820-3824)
IEEE DOI 1402
image registration. Change detection BibRef

Fytsilis, A.L.[Anastasios L.], Prokos, A.[Anthony], Koutroumbas, K.D.[Konstantinos D.], Michail, D.[Dimitrios], Kontoes, C.C.[Charalambos C.],
A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images,
PandRS(119), No. 1, 2016, pp. 165-186.
Elsevier DOI 1610
Unsupervised change detection BibRef

Hedjam, R., Kalacska, M., Mignotte, M., Ziaei Nafchi, H., Cheriet, M.,
Iterative Classifiers Combination Model for Change Detection in Remote Sensing Imagery,
GeoRS(54), No. 12, December 2016, pp. 6997-7008.
IEEE DOI 1612
geophysical image processing BibRef

Gong, M., Zhang, P., Su, L., Liu, J.,
Coupled Dictionary Learning for Change Detection From Multisource Data,
GeoRS(54), No. 12, December 2016, pp. 7077-7091.
IEEE DOI 1612
feature extraction BibRef

Su, L.Z.[Lin-Zhi], Gong, M.[Maoguo], Zhang, P.Z.[Pu-Zhao], Zhang, M.Y.[Ming-Yang], Liu, J.[Jia], Yang, H.[Hailun],
Deep learning and mapping based ternary change detection for information unbalanced images,
PR(66), No. 1, 2017, pp. 213-228.
Elsevier DOI 1704
Change detection BibRef

Lu, X., Yuan, Y., Zheng, X.,
Joint Dictionary Learning for Multispectral Change Detection,
Cyber(47), No. 4, April 2017, pp. 884-897.
IEEE DOI 1704
Dictionaries BibRef

Liu, Q.J.[Qing-Jie], Liu, L.[Lining], Wang, Y.H.[Yun-Hong],
Unsupervised Change Detection for Multispectral Remote Sensing Images Using Random Walks,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Xu, Y.[Yong], Lin, L.[Lin], Meng, D.Y.[De-Yu],
Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef


Sahbi, H.,
Misalignment resilient CCA for interactive satellite image change detection,
ICPR16(3326-3331)
IEEE DOI 1705
Correlation, Covariance matrices, Linear programming, Radio frequency, Robustness, Satellite broadcasting, Satellites BibRef

Möller, T., Nilssen, I., Nattkemper, T.W.,
Change detection in marine observatory image streams using Bi-Domain Feature Clustering,
ICPR16(793-798)
IEEE DOI 1705
Clustering algorithms, Feature extraction, Image color analysis, Image segmentation, Image sequences, Merging, Monitoring BibRef

Touati, R., Mignotte, M.,
A multidimensional scaling optimization and fusion approach for the unsupervised change detection problem in remote sensing images,
IPTA16(1-6)
IEEE DOI 1703
feature extraction BibRef

Paci, F.[Francesco], Baraldi, L.[Lorenzo], Serra, G.[Giuseppe], Cucchiara, R.[Rita], Benini, L.[Luca],
Context Change Detection for an Ultra-Low Power Low-Resolution Ego-Vision Imager,
Egocentric16(I: 589-602).
Springer DOI 1611
BibRef

Miron, A., Badii, A.,
Change detection based on graph cuts,
WSSIP15(273-276)
IEEE DOI 1603
Gaussian processes BibRef

Feng, W., Tian, F.P., Zhang, Q., Zhang, N., Wan, L., Sun, J.,
Fine-Grained Change Detection of Misaligned Scenes with Varied Illuminations,
ICCV15(1260-1268)
IEEE DOI 1602
Cameras BibRef

Stephane, M., Charlotte, P.,
Primal sketch of image series with edge preserving filtering application to change detection,
MultiTemp15(1-4)
IEEE DOI 1511
adaptive filters BibRef

Rodrigues, M.T.A.[Marco Túlio Alves], Balbino, D.[Daniel], Nascimentoo, E.R.[Erickson Rangel], Schwartz, W.R.[William Robson],
A Non-parametric Approach to Detect Changes in Aerial Images,
CIARP15(116-124).
Springer DOI 1511
BibRef

Jones, Z.[Ziggy], Brookes, M.[Mike], Dragotti, P.L.[Pier Luigi], Benton, D.[David],
Wide-baseline image change detection,
ICIP14(1589-1593)
IEEE DOI 1502
Approximation methods BibRef

Lira, J.[Jorge], Marín, E.[Erick],
Morphological Change of a Scene Employing Synthetic Multispectral and Panchromatic Images,
CASI14(1006-1013).
Springer DOI 1411
BibRef

Atasever, U.H., Civicioglu, P., Besdok, E., Ozkan, C.,
A New Unsupervised Change Detection Approach Based On DWT Image Fusion And Backtracking Search Optimization Algorithm For Optical Remote Sensing Data,
Thematic14(15-18).
DOI Link 1404
BibRef

Mayer, B.A.[Brandon A.], Mundy, J.L.[Joseph L.],
Change Point Geometry for Change Detection in Surveillance Video,
SCIA15(377-387).
Springer DOI 1506
BibRef
Earlier:
Duration Dependent Codebooks for Change Detection,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

De Gregorio, M.[Massimo], Giordano, M.[Maurizio],
Background Modeling by Weightless Neural Networks,
SBMI15(493-501).
Springer DOI 1511
BibRef

De Gregorio, M.[Massimo], Giordano, M.[Maurizio],
Change Detection with Weightless Neural Networks,
CDW14(409-413)
IEEE DOI 1409
Change Detection; Weightless Neural Networks BibRef

Faithfull, W.J.[William J.], Kuncheva, L.I.[Ludmila I.],
On Optimum Thresholding of Multivariate Change Detectors,
SSSPR14(364-373).
Springer DOI 1408
BibRef

Pichaikuppan, V.R.A.[Vijay Rengarajan Angarai], Narayanan, R.A.[Rajagopalan Ambasamudram], Rangarajan, A.[Aravind],
Change Detection in the Presence of Motion Blur and Rolling Shutter Effect,
ECCV14(VII: 123-137).
Springer DOI 1408
BibRef

Gressin, A.[Adrien], Vincent, N.[Nicole], Mallet, C.[Clément], Paparoditis, N.[Nicolas],
Semantic Approach in Image Change Detection,
ACIVS13(450-459).
Springer DOI 1311
BibRef

St.Charles, P.L.[Pierre-Luc], Bilodeau, G.A.[Guillaume-Alexandre],
Improving background subtraction using Local Binary Similarity Patterns,
WACV14(509-515)
IEEE DOI 1406
Analytical models BibRef

Bilodeau, G.A.[Guillaume-Alexandre], Jodoin, J.P.[Jean-Philippe], Saunier, N.[Nicolas],
Change Detection in Feature Space Using Local Binary Similarity Patterns,
CRV13(106-112)
IEEE DOI 1308
Binary codes BibRef

Kuncheva, L.I.[Ludmila I.], Faithfull, W.J.[William J.],
PCA feature extraction for change detection in multidimensional unlabelled streaming data,
ICPR12(1140-1143).
WWW Link. 1302
BibRef

Wu, Z., Hu, Z., Fan, Q.,
Superpixel-based Unsupervised Change Detection Using Multi-dimensional Change Vector Analysis and Svm-based Classification,
AnnalsPRS(I-7), No. 2012, pp. 257-262.
HTML Version. 1209
BibRef

Lin, Y., Liu, B., Lv, Q.l., Pan, C., Lu, Y.,
A Change Detection Method for Remote Sensing Image Based On Multi-feature Differencing Kernel SVM,
AnnalsPRS(I-7), No. 2012, pp. 227-235.
HTML Version. 1209
BibRef

Tweed, D.S.[David S.], Ferryman, J.M.[James M.],
Enhancing change detection in low-quality surveillance footage using markov random fields,
VNBA08(23-30).
DOI Link 1208
Urban surveillance. harsh lighting and reflective scenes. BibRef

Muralidharan, P.[Prasanna], Fletcher, P.T.[P. Thomas],
Sasaki metrics for analysis of longitudinal data on manifolds,
CVPR12(1027-1034).
IEEE DOI 1208
BibRef

Goyette, N.[Nil], Jodoin, P.M.[Pierre-Marc], Porikli, F.M.[Fatih M.], Konrad, J.[Janusz], Ishwar, P.[Prakash],
Changedetection.net: A new change detection benchmark dataset,
CDW12(1-8).
IEEE DOI 1207
Dataset, Change Detection. BibRef

Thomas, J.[Jim], Bowyer, K.W.[Kevin W.], Kareem, A.[Ahsan],
Color balancing for change detection in multitemporal images,
WACV12(385-390).
IEEE DOI 1203
BibRef

Fang, L.Y.[Le-Yuan], Li, S.T.[Shu-Tao], Hu, J.W.[Jian-Wen],
Multitemporal image change detection with compressed sparse representation,
ICIP11(2673-2676).
IEEE DOI 1201
BibRef

Gong, X.[Xing], Corpetti, T.[Thomas],
Adaptive patches for change detection,
ICIP11(2789-2792).
IEEE DOI 1201
BibRef

Lefebvre, A.[Antoine], Corpetti, T.[Thomas], Moy, L.H.[Laurence Hubert],
A measure for change detection in very high resolution remote sensing images based on texture analysis,
ICIP09(1697-1700).
IEEE DOI 0911
BibRef

Abdelrahman, M.A.[Mostafa A.], Ali, A.M.[Asem M.], Elhabian, S.Y.[Shireen Y.], Farag, A.A.[Aly A.],
Solving Geometric Co-registration Problem of Multi-spectral Remote Sensing Imagery Using SIFT-Based Features toward Precise Change Detection,
ISVC11(II: 607-616).
Springer DOI 1109
BibRef

Cui, S.Y.[Shi-Yong], Datcu, M.[Mihai],
Coarse to fine patches-based multitemporal analysis of very high resolution satellite images,
MultiTemp11(85-88).
IEEE DOI 1109
Patch based change detection. BibRef

Petitjean, F.[Francois], Inglada, J.[Jordi], Gancarskv, P.[Pierre],
Clustering of satellite image time series under Time Warping,
MultiTemp11(69-72).
IEEE DOI 1109
Land use change maps, but weather interrupts the time sequence. BibRef

Ding, S., Rulinda, C.M., Stein, A., Bijker, W.,
NDVI time series and Markov chains to model the change of fuzzy vegetative drought classes,
MultiTemp11(201-204).
IEEE DOI 1109
BibRef

Resta, S.[Salvatore], Acito, N.[Nicola], Diani, M.[Marco], Corsini, G.[Giovanni], Opsahl, T.[Thomas], Haavardsholm, T.V.[Trym Vegard],
Detection of small changes in airborne hyperspectral imagery: Experimental results over urban areas,
MultiTemp11(5-8).
IEEE DOI 1109
BibRef

Briassouli, A.[Alexia], Kompatsiaris, I.[Ioannis],
Change Detection for Temporal Texture in the Fourier Domain,
ACCV10(I: 149-160).
Springer DOI 1011
BibRef

Kovacs, A.[Andrea], Sziranyi, T.[Tamas],
New Saliency Point Detection and Evaluation Methods for Finding Structural Differences in Remote Sensing Images of Long Time-Span Samples,
ACIVS10(II: 272-283).
Springer DOI 1012
BibRef

Milisavljevic, N.[Nada], Closson, D.[Damien], Bloch, I.[Isabelle],
Detecting potential human activities using coherent change detection,
IPTA10(482-485).
IEEE DOI 1007
BibRef

Sun, K.M.[Kai-Ming], Sui, H.G.[Hai-Gang], Li, D.R.[De-Ren], Xu, C.[Chuan],
A New Relative Radiometric Consistency Processing Method For Change Detection Based On Wavelet Transform And Low-pass Filter,
VCGVA09(xx-yy). 0910
wavelet transform; radiometric normalization; low-pass filter; change detection BibRef

Emary, E.[Eid], Mostafa, K.[Khaled], Onsi, H.[Hoda],
A proposedmulti-scale approach with automatic scale selection for image change detection,
ICIP09(3185-3188).
IEEE DOI 0911
BibRef

Buades, A., Lisani, J.L., Rudin, L.,
Adaptive Change Detection,
WSSIP09(1-4).
IEEE DOI 0906
BibRef

Mamun, A.[Al], Jia, X.P.[Xiu-Ping], Ryan, M.[Michael],
Combined Time Domain and Spectral Domain Data Compression for Fast Multispectral Imagery Updating,
DICTA09(285-290).
IEEE DOI 0912
BibRef
Earlier:
Sequential Transmission of Remote Sensing Data Using a Linear Model to Update Change,
DICTA08(104-110).
IEEE DOI 0812
BibRef

Theiler, J.[James], Adler-Golden, S.M.,
Detection of ephemeral changes in sequences of images,
AIPR08(1-8).
IEEE DOI 0810
BibRef

Theiler, J.[James],
Subpixel Anomalous Change Detection in Remote Sensing Imagery,
Southwest08(165-168).
IEEE DOI 0803
BibRef

Tahmoush, D.,
Image Differencing Approaches to Medical Image Classification,
AIPR07(22-27).
IEEE DOI 0710
BibRef

Becker, N.M., Brumby, S., David, N.A., Irvine, J.M.,
Analysis of multispectral imagery and modeling contaminant transport,
AIPR02(71-77).
IEEE DOI 0210
BibRef

Ray, N.[Nilanjan], Saha, B.N.[Baidya Nath], Zhang, H.[Hong],
Change Detection and Object Segmentation: A Histogram of Features-Based Energy Minimization Approach,
ICCVGIP08(628-635).
IEEE DOI 0812
BibRef

Miezianko, R.[Roland], Pokrajac, D.[Dragoljub],
Detecting changes in multilayered orthoimages with spatiotemporal texture blocks,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Hulkkonen, J.[Jenni], Heikkonen, J.[Jukka],
A minimum description length principle based method for signal change detection in machine condition monitoring,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Fournier, A.[Alexandre], Weiss, P.[Pierre], Blanc-Feraud, L.[Laure], Aubert, G.[Gilles],
A contrast equalization procedure for change detection algorithms: Applications to remotely sensed images of urban areas,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Sezer, O.G.[Osman G.], Mundy, J.L.[Joseph L.], Altunbasak, Y.[Yucel], Cooper, D.B.[David B.],
NorMaL: Non-compact Markovian Likelihood for change detection,
ICPR08(1-4).
IEEE DOI 0812
BibRef

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 0812
BibRef

Li, Z.[Zhi], Liu, G.Z.[Gui-Zhong],
A novel scene change detection algorithm based on the 3D wavelet transform,
ICIP08(1536-1539).
IEEE DOI 0810
BibRef

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 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 0806
BibRef

Ozay, N.[Necmiye], Sznaier, M.[Mario], Camps, O.I.[Octavia I.],
Sequential sparsification for change detection,
CVPR08(1-6).
IEEE DOI 0806
BibRef

Singh, M.[Maneesh], Parameswaran, V.[Vasu], Ramesh, V.[Visvanathan],
Order consistent change detection via fast statistical significance testing,
CVPR08(1-8).
IEEE DOI 0806
BibRef

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).
IEEE DOI 0610
BibRef

Candocia, F.M., Mandarino, D.,
Change Detection on Comparametrically Related Images,
ICIP06(1073-1076).
IEEE DOI 0610
BibRef

Ribnick, E., Atev, S., Masoud, O.T., Papanikolopoulos, N.P., Voyles, R.,
Real-Time Detection of Camera Tampering,
AVSBS06(10-10).
IEEE DOI 0611
Tampering based on large differences between old and new frames. BibRef

Sato, J.[Junji], Takahashi, T.[Tomokazu], Ide, I.[Ichiro], Murase, H.[Hiroshi],
Change detection in streetscapes from GPS coordinated omni-directional image sequences,
ICPR06(IV: 935-938).
IEEE DOI 0609
BibRef

Li, W.M.[Wei-Ming], Li, X.M.[Xiao-Ming], Wu, Y.H.[Yi-Hong], Hu, Z.Y.[Zhan-Yi],
A Novel Framework for Urban Change Detection Using VHR Satellite Images,
ICPR06(II: 312-315).
IEEE DOI 0609
BibRef

Kita, Y.[Yasuyo],
A study of change detection from satellite images using joint intensity histogram,
ICPR08(1-4).
IEEE DOI 0812
BibRef
Earlier:
Change detection using joint intensity histogram,
ICPR06(II: 351-356).
IEEE DOI 0609
BibRef

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 0509
BibRef

Harasse, S., Bonnaud, L., Caplier, A., Desvignes, M.,
Automated camera dysfunctions detection,
Southwest04(36-40).
WWW Link. 0411
Detect changes that indicate the camera is not working. BibRef

Qiu, B., Prinet, V., Perrier, E., Monga, O.,
Multi-block PCA method for image change detection,
CIAP03(385-390).
IEEE DOI 0310
BibRef

Lisani, J.L., Morel, J.M.,
Detection of major changes in satellite images,
ICIP03(I: 941-944).
IEEE DOI 0312
BibRef

de Geyter, M., Philips, W.,
A noise robust method for change detection,
ICIP03(II: 391-394).
IEEE DOI 0312
BibRef

Latecki, L.J., Wen, X.D.[Xiang-Dong], Ghubade, N.,
Detection of changes in surveillance videos,
AVSBS03(237-242).
WWW Link. 0310
BibRef

Brocke, M.,
Statistical Image Sequence Processing for Temporal Change Detection,
DAGM02(215 ff.).
Springer DOI 0303
BibRef

Huwer, S., Niemann, H.,
Adaptive Change Detection for Real-Time Surveillance Applications,
VS00(xx-yy). 0102
BibRef

Tompa, D., Morton, J., Jernigan, E.,
Perceptually Based Image Comparison,
ICIP00(Vol I: 489-492).
IEEE DOI 0008
BibRef

Angiati, D., Gera, G., Piva, S., Regazzoni, C.S.,
A novel method for graffiti detection using change detection algorithm,
AVSBS05(242-246).
IEEE DOI 0602
BibRef

Marcenaro, L., Oberti, F., Regazzoni, C.S.,
Change Detection Methods for Automatic Scene Analysis by Using Mobile Surveillance Cameras,
ICIP00(Vol I: 244-247).
IEEE DOI 0008
BibRef

Capel, D., Zisserman, A., Bramble, S., and Compton, D.,
An Automatic Method for the Removal of Unwanted, Non-periodic Patterns from Forensic Images,
SPIE(3576), 1-6 November, 1998. pp. xx-yy.
PS File. BibRef 9811

Heikkonen, J., Varjo, J., Vehtari, A.,
Forest Change Detection via Landsat TM Difference Features,
SCIA99(Remote Sensing). BibRef 9900

Lu, W., Doihara, T., Matsumoto, Y.,
Detection of Building Changes from Aerial Images Through Information Fusion,
MVA98(xx-yy). BibRef 9800

Sugano, M., Nakajima, Y., Yanagihara, H., Yoneyama, A.,
A fast scene change detection on MPEG coding parameter domain,
ICIP98(I: 888-892).
IEEE DOI 9810
BibRef

Wiemker, R.[Rafael],
An iterative spectral-spatial Bayesian labeling approach for unsupervised robust change detection on remotely sensed multispectral imagery,
CAIP97(263-270).
Springer DOI 9709
BibRef

Sutherland, K., Rutovitz, D., Bell, J.E., Ironside, J.W.,
Evaluation of a novel application of image analysis to spongiform change detection,
ICIP94(I: 378-381).
IEEE DOI 9411
BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Radar, SAR Image Change Detection .


Last update:Sep 18, 2017 at 11:34:11