12.1.5 Change Detection -- Image Level

Chapter Contents (Back)
Change Detection. Some of the error analysis:
See also Radar, SAR Image Change Detection.
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

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,
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Elsevier DOI 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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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

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Morisette, J.T.[Jeffrey T.], Khorram, S.[Siamak],
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Smits, P.C., Annoni, A.,
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Li, L.Y.[Li-Yuan], Leung, M.K.H.,
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Bromiley, P.A., Thacker, N.A., Courtney, P.,
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Rosin, P.L.[Paul L.],
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Carrao, H., Gonsalves, P., Caetano, M.,
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Ilsever, M.[Murat], Ünsalan, C.[Cem],
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Change-detection based on support vector data description handling dependency,
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Subudhi, B.N.[Badri Narayan], Ghosh, S.[Susmita], Ghosh, A.[Ashish],
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Jin, J.H.[Jung-Hwan], Shin, H.J.[Hyun Joon], Choi, J.J.[Jung-Ju],
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Elsevier DOI 1309
Image phylogeny tree BibRef

Kujawinska, M.[Malgorzata], Malesa, M.[Marcin], Malowany, K.[Krzysztof],
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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

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IEEE DOI 1311
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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

Lingg, A.J., Zelnio, E., Garber, F., Rigling, B.D.,
A Sequential Framework for Image Change Detection,
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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.
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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.,
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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
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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
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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
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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

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.[Massimo], 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, 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

Ye, Y., Bruzzone, L., Shan, J., Bovolo, F., Zhu, Q.,
Fast and Robust Matching for Multimodal Remote Sensing Image Registration,
GeoRS(57), No. 11, November 2019, pp. 9059-9070.
IEEE DOI 1911
Feature extraction, Remote sensing, Image matching, Histograms, Frequency-domain analysis, Shape, Image registration, pixelwise feature representation 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

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
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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

Minematsu, T., Shimada, A.[Atsushi], Taniguchi, R.I.[Rin-Ichiro],
Analytics of deep neural network in change detection,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, feedforward neural nets, learning (artificial intelligence), DNN, background features, Training 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

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

Bu, L., Zhao, D., Alippi, C.,
An Incremental Change Detection Test Based on Density Difference Estimation,
SMCS(47), No. 10, October 2017, pp. 2714-2726.
IEEE DOI 1709
Estimation, Feature extraction, Histograms, Kernel, Probability density function, Training, Change detection, incremental computing, BibRef

Seo, D.K.[Dae Kyo], Kim, Y.H.[Yong Hyun], Eo, Y.D.[Yang Dam], Park, W.Y.[Wan Yong], Park, H.C.[Hyun Chun],
Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection,
RS(9), No. 11, 2017, pp. xx-yy.
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Yan, L.[Li], Xia, W.[Wang], Zhao, Z.[Zhan], Wang, Y.[Yanran],
A Novel Approach to Unsupervised Change Detection Based on Hybrid Spectral Difference,
RS(10), No. 6, 2018, pp. xx-yy.
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Wang, Q.[Qi], Yuan, Z.H.[Zheng-Hang], Du, Q.[Qian], Li, X.L.[Xue-Long],
GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection,
GeoRS(57), No. 1, January 2019, pp. 3-13.
IEEE DOI 1901
Hyperspectral imaging, Machine learning, Task analysis, Neural networks, Principal component analysis, spectral unmixing BibRef

Li, X.L.[Xue-Long], Yuan, Z.H.[Zheng-Hang], Wang, Q.[Qi],
Unsupervised Deep Noise Modeling for Hyperspectral Image Change Detection,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
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Jabari, S.[Shabnam], Rezaee, M.[Mohammad], Fathollahi, F.[Fatemeh], Zhang, Y.[Yun],
Multispectral change detection using multivariate Kullback-Leibler distance,
PandRS(147), 2019, pp. 163-177.
Elsevier DOI 1901
Urban change detection, Multivariate Kullback-Leibler distance, Non-linear change criterion BibRef

Tan, K.[Kun], Zhang, Y.[Yusha], Wang, X.[Xue], Chen, Y.[Yu],
Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
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Kwan, C.[Chiman], Ayhan, B.[Bulent], Larkin, J.[Jude], Kwan, L.[Liyun], Bernabé, S.[Sergio], Plaza, A.[Antonio],
Performance of Change Detection Algorithms Using Heterogeneous Images and Extended Multi-attribute Profiles (EMAPs),
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
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Huang, J.[Jiru], Liu, Y.[Yang], Wang, M.[Min], Zheng, Y.[Yalan], Wang, J.[Jie], Ming, D.P.[Dong-Ping],
Change Detection of High Spatial Resolution Images Based on Region-Line Primitive Association Analysis and Evidence Fusion,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
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Yang, G., Li, H., Wang, W., Yang, W., Emery, W.J.,
Unsupervised Change Detection Based on a Unified Framework for Weighted Collaborative Representation With RDDL and Fuzzy Clustering,
GeoRS(57), No. 11, November 2019, pp. 8890-8903.
IEEE DOI 1911
Collaboration, Dictionaries, Clustering algorithms, Change detection algorithms, Feature extraction, unsupervised change detection BibRef

Luppino, L.T., Bianchi, F.M., Moser, G., Anfinsen, S.N.,
Unsupervised Image Regression for Heterogeneous Change Detection,
GeoRS(57), No. 12, December 2019, pp. 9960-9975.
IEEE DOI 1912
Kernel, Manifolds, Sensors, Correlation, Dictionaries, Satellites, Support vector machines, Affinity matrix, Gaussian process (GP), unsupervised change detection (CD) BibRef

Du, P.J.[Pei-Jun], Wang, X.[Xin], Chen, D.M.[Dong-Mei], Liu, S.C.[Si-Cong], Lin, C.[Cong], Meng, Y.P.[Ya-Ping],
An improved change detection approach using tri-temporal logic-verified change vector analysis,
PandRS(161), 2020, pp. 278-293.
Elsevier DOI 2002
Change detection, Tri-temporal image, Change vector analysis (CVA), Logic reasoning BibRef

Hou, B., Liu, Q., Wang, H., Wang, Y.,
From W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques,
GeoRS(58), No. 3, March 2020, pp. 1790-1802.
IEEE DOI 2003
Change detection, change detection generative adversarial network (CDGAN), W-Net BibRef

Han, Y.Y.[Youk-Yung], Javed, A.[Aisha], Jung, S.J.[Se-Jung], Liu, S.[Sicong],
Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster-Shafer Theory,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
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Shi, W.Z.[Wen-Zhong], Zhang, M.[Min], Zhang, R.[Rui], Chen, S.X.[Shan-Xiong], Zhan, Z.[Zhao],
Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges,
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Li, D.W.[Da-Wei], Yan, S.Y.[Si-Yuan], Zhao, M.B.[Ming-Bo], Chow, T.W.S.[Tommy W. S.],
Spatiotemporal Tree Filtering for Enhancing Image Change Detection,
IP(29), 2020, pp. 8805-8820.
IEEE DOI 2009
Maximum likelihood detection, Nonlinear filters, Information filters, Filtering theory, Spatiotemporal phenomena, post-processing BibRef

Shi, N.[Nian], Chen, K.[Keming], Zhou, G.Y.[Guang-Yao], Sun, X.[Xian],
A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images,
RS(12), No. 18, 2020, pp. xx-yy.
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Mandal, M.[Murari], Dhar, V.[Vansh], Mishra, A.[Abhishek], Vipparthi, S.K.[Santosh Kumar], Abdel-Mottaleb, M.[Mohamed],
3DCD: Scene Independent End-to-End Spatiotemporal Feature Learning Framework for Change Detection in Unseen Videos,
IP(30), 2021, pp. 546-558.
IEEE DOI 2012
Videos, Spatiotemporal phenomena, Feature extraction, Training, Estimation, Adaptation models, deep learning BibRef

Liu, N.[Ning], Guo, B.[Bin], Li, X.J.[Xin-Ju], Min, X.Y.[Xiang-Yu],
Gradient clustering algorithm based on deep learning aerial image detection,
PRL(141), 2021, pp. 37-44.
Elsevier DOI 2101
Aerial image detection, Deep learning, Gradient clustering algorithm, Aerial image BibRef

Zhan, T.M.[Tian-Ming], Song, B.[Bo], Xu, Y.[Yang], Wan, M.H.[Ming-Hua], Wang, X.[Xin], Yang, G.[Guowei], Wu, Z.[Zebin],
SSCNN-S: A Spectral-Spatial Convolution Neural Network with Siamese Architecture for Change Detection,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
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Guo, Q.L.[Qing-Le], Zhang, J.P.[Jun-Ping], Zhang, Y.[Ye],
Multitemporal Images Change Detection Based on AMMF and Spectral Constraint Strategy,
GeoRS(59), No. 4, April 2021, pp. 3444-3457.
IEEE DOI 2104
Correlation, Adaptation models, Image segmentation, Feature extraction, Matrix decomposition, Gaussian distribution, stepwise subtraction BibRef

Guo, Q.L.[Qing-Le], Zhang, J.P.[Jun-Ping], Zhang, Y.[Ye],
Multitemporal Hyperspectral Images Change Detection Based on Joint Unmixing and Information Coguidance Strategy,
GeoRS(59), No. 11, November 2021, pp. 9633-9645.
IEEE DOI 2111
Hyperspectral imaging, Perturbation methods, Feature extraction, Training, Optimization, Task analysis, Data mining, multitemporal information coguidance BibRef

Ruff, L.[Lukas], Kauffmann, J.R.[Jacob R.], Vandermeulen, R.A.[Robert A.], Montavon, G.[Grégoire], Samek, W.[Wojciech], Kloft, M.[Marius], Dietterich, T.G.[Thomas G.], Müller, K.R.[Klaus-Robert],
A Unifying Review of Deep and Shallow Anomaly Detection,
PIEEE(109), No. 5, May 2021, pp. 756-795.
IEEE DOI 2105
Deep learning, Principal component analysis, Neural networks, Kernel, Anomaly detection, Data models, Task analysis, unsupervised learning. BibRef

He, Y.X.[You-Xi], Jia, Z.H.[Zhen-Hong], Yang, J.[Jie], Kasabov, N.K.[Nikola K.],
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Shao, P.[Pan], Shi, W.Z.[Wen-Zhong], Liu, Z.W.[Zhe-Wei], Dong, T.[Ting],
Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting,
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Peng, X.L.[Xue-Li], Zhong, R.F.[Ruo-Fei], Li, Z.[Zhen], Li, Q.Y.[Qing-Yang],
Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference,
GeoRS(59), No. 9, September 2021, pp. 7296-7307.
IEEE DOI 2109
Optical design, Optical computing, Network architecture, Feature extraction, Optical imaging, Optical network units, optical remote sensing image BibRef

Huang, R.[Rui], Xing, Y.[Yan], Zhou, M.[Mo], Wang, R.F.[Ruo-Fei],
Change detection with cross enhancement of high- and low-level change-related features,
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DOI Link 2110
absolute difference, change detection, change-related feature, cross feature enhancement BibRef

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Change Detection Using a Texture Feature Space Outlier Index from Mono-Temporal Remote Sensing Images and Vector Data,
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Andermatt, P.[Philipp], Timofte, R.[Radu],
A Weakly Supervised Convolutional Network for Change Segmentation and Classification,
MLCSA20(103-119).
Springer DOI 2103
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Pilarska, M.,
Hierarchical Approach for Detecting Changes with the Use of Different Pyramid Levels In Dense Image Matching,
ISPRS20(B3:1615-1620).
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Seydi, S.T., Hasanlou, M.,
Binary Hyperspectral Change Detection Based on 3d Convolution Deep Learning,
ISPRS20(B3:1629-1633).
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ISPRS20(B3:1635-1643).
DOI Link 2012
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Shi, X.X.[Xiang-Xi], Yang, X.[Xu], Gu, J.X.[Jiu-Xiang], Joty, S.[Shafiq], Cai, J.F.[Jian-Fei],
Finding It at Another Side: A Viewpoint-adapted Matching Encoder for Change Captioning,
ECCV20(XIV:574-590).
Springer DOI 2011
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Putri, A.R.D., Sidiropoulos, P., Muller, J.P.,
Anomaly Detection Performance Comparison On Anomaly-detection Based Change Detection On Martian Image Pairs,
PRSM19(1437-1441).
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Varghese, A.[Ashley], Gubbi, J.[Jayavardhana], Ramaswamy, A.[Akshaya], Balamuralidhar, P.,
ChangeNet: A Deep Learning Architecture for Visual Change Detection,
CVUAV18(II:129-145).
Springer DOI 1905
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Chen, Y., Ouyang, X., Agam, G.,
MFCNET: End-to-End Approach for Change Detection in Images,
ICIP18(4008-4012)
IEEE DOI 1809
Training, Feature extraction, Machine learning, Image segmentation, Convolutional neural networks, Task analysis, Change detection, MFCNet BibRef

Caye Daudt, R., Le Saux, B., Boulch, A.,
Fully Convolutional Siamese Networks for Change Detection,
ICIP18(4063-4067)
IEEE DOI 1809
Cats, Earth, Training, Computer architecture, Machine learning, Image analysis, Change detection algorithms, Change detection, Earth observation BibRef

Gubbi, J.[Jayavardhana], Ramaswamy, A.[Akshaya], Sandeep, N.K., Varghese, A.[Ashley], Balamuralidhar, P.,
Visual Change Detection Using Multiscale Super Pixel,
DICTA17(1-6)
IEEE DOI 1804
control engineering computing, image classification, image matching, image segmentation, inspection, Visualization BibRef

Liang, D., Kaneko, S., Sun, H., Kang, B.,
Adaptive local spatial modeling for online change detection under abrupt dynamic background,
ICIP17(2020-2024)
IEEE DOI 1803
Adaptation models, Aerodynamics, Color, Correlation, Lighting, Robustness, Training, Background model, change detection, illumination variation BibRef

Tan, Y., Das, S., Chaudhry, A.,
An aerial change detection system using multiple detector fusion and adaboost classification,
ICIP17(2637-2641)
IEEE DOI 1803
Detectors, Feature extraction, Histograms, Pipelines, Real-time systems, Robustness, Streaming media, Aerial Image, Fusion BibRef

Huang, R., Feng, W., Wang, Z., Fan, M., Wan, L., Sun, J.,
Learning to Detect Fine-Grained Change Under Variant Imaging Conditions,
eHeritage17(2916-2924)
IEEE DOI 1802
Adaptive optics, Cameras, DSL, Detectors, Lighting, Optical imaging, Training BibRef

Bianco, S.[Simone], Ciocca, G.[Gianluigi], Schettini, R.[Raimondo],
How Far Can You Get by Combining Change Detection Algorithms?,
CIAP17(I:96-107).
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Fusing multiple change algorithms. BibRef

Sahbi, H.[Hichem],
Learning CCA Representations for Misaligned Data,
CEFR-LCV18(IV:468-485).
Springer DOI 1905
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Earlier:
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

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
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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
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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,
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Springer DOI 1411
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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
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Earlier:
Duration Dependent Codebooks for Change Detection,
BMVC14(xx-yy).
HTML Version. 1410
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de Gregorio, M.[Massimo], Giordano, M.[Maurizio],
Background Modeling by Weightless Neural Networks,
SBMI15(493-501).
Springer DOI 1511
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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
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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
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Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Change Detection for Remote Sensing Image Level .


Last update:Nov 30, 2021 at 22:19:38