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.
See also Change Detection for Hyperspectral Images.

Caye-Daudt, R.[Rodrigo], Le Saux, B.[Bertrand], Boulch, A.[Alexandre], Gousseau, Y.[Yann],
Onera Satellite Change Detection (OSCD) Database,
2018 Dataset, Change Detection.
WWW Link.
WWW Link.
See also Fully Convolutional Siamese Networks for Change Detection. BibRef

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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Minematsu, T., Shimada, A.[Atsushi], Taniguchi, R.I.[Rin-Ichiro],
Analytics of deep neural network in change detection,
AVSS17(1-6)
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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.],
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False discovery rate approach to image change detection,
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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,
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Unsupervised change detection BibRef

Gong, M., Zhang, P., Su, L., Liu, J.,
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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],
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Change detection BibRef

Lu, X., Yuan, Y., Zheng, X.,
Joint Dictionary Learning for Multispectral Change Detection,
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Bu, L., Zhao, D., Alippi, C.,
An Incremental Change Detection Test Based on Density Difference Estimation,
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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],
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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],
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Yang, G., Li, H., Wang, W., Yang, W., Emery, W.J.,
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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],
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Change detection, Tri-temporal image, Change vector analysis (CVA), Logic reasoning BibRef

Hou, B., Liu, Q., Wang, H., Wang, Y.,
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IEEE DOI 2003
Change detection, change detection generative adversarial network (CDGAN), W-Net BibRef

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IEEE DOI 2009
Maximum likelihood detection, Nonlinear filters, Information filters, Filtering theory, Spatiotemporal phenomena, post-processing BibRef

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IEEE DOI 2012
Videos, Spatiotemporal phenomena, Feature extraction, Training, Estimation, Adaptation models, deep learning BibRef

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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.W.[Guo-Wei], Wu, Z.B.[Ze-Bin],
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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],
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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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Peng, X.L.[Xue-Li], Zhong, R.F.[Ruo-Fei], Li, Z.[Zhen], Li, Q.Y.[Qing-Yang],
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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

Wei, D.S.[Dong-Sheng], Hou, D.Y.[Dong-Yang], Zhou, X.G.[Xiao-Guang], Chen, J.[Jun],
Change Detection Using a Texture Feature Space Outlier Index from Mono-Temporal Remote Sensing Images and Vector Data,
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Ren, C.J.[Cai-Jun], Wang, X.Y.[Xiang-Yu], Gao, J.[Jian], Zhou, X.R.[Xi-Ren], Chen, H.H.[Huan-Huan],
Unsupervised Change Detection in Satellite Images With Generative Adversarial Network,
GeoRS(59), No. 12, December 2021, pp. 10047-10061.
IEEE DOI 2112
Feature extraction, Generative adversarial networks, Deep learning, Satellites, Task analysis, unsupervised BibRef

Zheng, Z.[Zhuo], Zhong, Y.F.[Yan-Fei], Tian, S.Q.[Shi-Qi], Ma, A.L.[Ai-Long], Zhang, L.P.[Liang-Pei],
ChangeMask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection,
PandRS(183), 2022, pp. 228-239.
Elsevier DOI 2201
Multi-task learning, Temporal symmetry, Change detection, Deep learning, Remote sensing, Multi-temporal, Semantic segmentation BibRef

Zheng, D.L.[Da-Long], Wei, Z.H.[Zhi-Hui], Wu, Z.B.[Ze-Bin], Liu, J.[Jia],
Learning Pairwise Potential CRFs in Deep Siamese Network for Change Detection,
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Mandal, M.[Murari], Vipparthi, S.K.[Santosh Kumar],
Scene Independency Matters: An Empirical Study of Scene Dependent and Scene Independent Evaluation for CNN-Based Change Detection,
ITS(23), No. 3, March 2022, pp. 2031-2044.
IEEE DOI 2203
Training, Deep learning, Feature extraction, Benchmark testing, Task analysis, Adaptation models, Change detection, deep learning BibRef

Huang, Q.B.[Qing-Bao], Liang, Y.[Yu], Wei, J.L.[Jie-Long], Cai, Y.[Yi], Liang, H.Y.[Han-Yu], Leung, H.F.[Ho-Fung], Li, Q.[Qing],
Image Difference Captioning With Instance-Level Fine-Grained Feature Representation,
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IEEE DOI 2204

WWW Link. Code, Change Detection. Feature extraction, Semantics, Visualization, Task analysis, Image color analysis, Proposals, Interference, similarity-based difference finding BibRef

Liang, Z.[Zheng], Zhu, B.[Bin], Zhu, Y.X.[Yao-Xuan],
High Resolution Representation-Based Siamese Network for Remote Sensing Image Change Detection,
IET-IPR(16), No. 9, 2022, pp. 2506-2517.
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Wang, Z.H.[Zhi-Heng], Li, S.Q.[Shi-Qiang], Wang, J.[Jili],
Multi-Scale Analysis for Coherent Change Detection: A Method for Extracting Typical Changed Area,
RS(14), No. 19, 2022, pp. xx-yy.
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Fang, S.[Shuai], Guo, Q.[Qing], Cao, Y.[Yang],
WDBSTF: A Weighted Dual-Branch Spatiotemporal Fusion Network Based on Complementarity between Super-Resolution and Change Prediction,
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Ouerghi, E.[Elyes],
A Deep Learning Model for Change Detection on Satellite Images,
IPOL(12), 2022, pp. 550-557.
DOI Link 2212

See also Fully Convolutional Siamese Networks for Change Detection. Onera Satellite Change Detection (OSCD) database
See also Onera Satellite Change Detection (OSCD) Database. BibRef

Wu, J.M.[Jin-Ming], Xie, C.H.[Chun-Hui], Zhang, Z.[Zuxi], Zhu, Y.X.[Yong-Xin],
A Deeply Supervised Attentive High-Resolution Network for Change Detection in Remote Sensing Images,
RS(15), No. 1, 2023, pp. xx-yy.
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Dagobert, T.[Tristan], Grompone-von Gioi, R.[Rafael], de Franchis, C.[Carlo], Hessel, C.[Charles],
Detection and Interpretation of Change in Registered Satellite Image Time Series,
IPOL(12), 2022, pp. 625-651.
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Code, Change Detection. BibRef

Wu, J.Z.[Jun-Zheng], Fu, R.G.[Rui-Gang], Liu, Q.[Qiang], Ni, W.P.[Wei-Ping], Cheng, K.[Kenan], Li, B.[Biao], Sun, Y.[Yuli],
A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images,
RS(15), No. 3, 2023, pp. xx-yy.
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Wang, G.H.[Guo-Hua], Gao, B.B.[Bin-Bin], Wang, C.J.[Cheng-Jie],
How to Reduce Change Detection to Semantic Segmentation,
PR(138), 2023, pp. 109384.
Elsevier DOI 2303
Change detection, Semantic segmentation, Feature fusion BibRef

Li, L.[Ling], Chen, C.[Chunyi], Peng, J.[Jun], Zhang, R.[Ripei],
Predicting visual difference maps for computer-generated images by integrating human visual system model and deep learning,
IET-IPR(17), No. 3, 2023, pp. 901-915.
DOI Link 2303
distortion visibility, human visual perception, image quality, visual metric BibRef

Tu, Y.B.[Yun-Bin], Li, L.[Liang], Su, L.[Li], Du, J.P.[Jun-Ping], Lu, K.[Ke], Huang, Q.M.[Qing-Ming],
Viewpoint-Adaptive Representation Disentanglement Network for Change Captioning,
IP(32), 2023, pp. 2620-2635.
IEEE DOI 2305
Task analysis, Image coding, Adaptation models, Encoding, Computer science, Transformers, Semantics, Change captioning, position-embedded representation learning BibRef

Wu, C.[Chen], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection,
PAMI(45), No. 8, August 2023, pp. 9774-9788.
IEEE DOI 2307
Task analysis, Image segmentation, Generators, Remote sensing, Generative adversarial networks, Predictive models, Training, weakly supervised segmentation BibRef

Jiang, L.[Liangcun], Li, F.[Feng], Huang, L.[Li], Peng, F.F.[Fei-Fei], Hu, L.[Lei],
TTNet: A Temporal-Transform Network for Semantic Change Detection Based on Bi-Temporal Remote Sensing Images,
RS(15), No. 18, 2023, pp. 4555.
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Saputra, B.A.[Bagus Aris], Lin, S.C.[Shih-Chun],
Byzantine Distributed Quickest Change Detection Based on Bounded-Distance-Decoding,
SPLetters(30), 2023, pp. 1532-1536.
IEEE DOI 2311
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Tu, Y.B.[Yun-Bin], Li, L.[Liang], Su, L.[Li], Lu, K.[Ke], Huang, Q.M.[Qing-Ming],
Neighborhood Contrastive Transformer for Change Captioning,
MultMed(25), 2023, pp. 9518-9529.
IEEE DOI 2312
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Yue, S.B.[Sheng-Bin], Tu, Y.[Yunbin], Li, L.[Liang], Yang, Y.[Ying], Gao, S.X.[Sheng-Xiang], Yu, Z.T.[Zheng-Tao],
I3N: Intra- and Inter-Representation Interaction Network for Change Captioning,
MultMed(25), 2023, pp. 8828-8841.
IEEE DOI 2312
BibRef

Song, Y.[Yabin], Xiang, J.[Jun], Jiang, J.W.[Jia-Wei], Yan, E.[Enping], Wei, W.[Wei], Mo, D.K.[Deng-Kui],
A Cross-Domain Change Detection Network Based on Instance Normalization,
RS(15), No. 24, 2023, pp. 5785.
DOI Link 2401
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Fan, R.[Rongbo], Xie, J.L.[Jia-Lin], Yang, J.H.[Jian-Hua], Hong, Z.L.[Zeng-Lin], Xu, Y.Q.[Yu-Qi], Hou, H.[Hong],
Multiscale Change Detection Domain Adaptation Model Based on Illumination-Reflection Decoupling,
RS(16), No. 5, 2024, pp. 799.
DOI Link 2403
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Chen, M.[Ming], Jiang, W.[Wanshou], Zhou, Y.[Yuan],
DTT-CGINet: A Dual Temporal Transformer Network with Multi-Scale Contour-Guided Graph Interaction for Change Detection,
RS(16), No. 5, 2024, pp. 844.
DOI Link 2403
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Sachdeva, R.[Ragav], Zisserman, A.[Andrew],
The Change You Want to See,
WACV23(3982-3991)
IEEE DOI 2302
Training, Solid modeling, Surveillance, Training data, Detectors, Benchmark testing BibRef

Noh, H.[Hyeoncheol], Ju, J.[Jingi], Seo, M.[Minseok], Park, J.[Jongchan], Choi, D.G.[Dong-Geol],
Unsupervised Change Detection Based on Image Reconstruction Loss,
EarthVision22(1351-1360)
IEEE DOI 2210
Codes, Semantics, Detectors, Benchmark testing, Pattern recognition BibRef

Kim, H.[Hoeseong], Kim, J.S.[Jong-Seok], Lee, H.[Hyungseok], Park, H.[Hyunsung], Kim, G.[Gunhee],
Viewpoint-Agnostic Change Captioning with Cycle Consistency,
ICCV21(2075-2084)
IEEE DOI 2203
Visualization, Image coding, Filtering, Neural networks, SPICE, Cameras, Vision + language, Scene analysis and understanding BibRef

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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Soto, P.J., Costa, G.A.O.P., Feitosa, R.Q., Happ, P.N., Ortega, M.X., Noa, J., Almeida, C.A., Heipke, C.,
Domain Adaptation with CycleGAN for Change Detection In the Amazon Forest,
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, Machine learning, Image analysis, Change detection algorithms, Change detection, Earth observation
See also Deep Learning Model for Change Detection on Satellite Images, A.
See also Onera Satellite Change Detection (OSCD) Database. 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).
Springer DOI 1711
Fusing multiple change algorithms. BibRef

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

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.
DOI Link 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

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

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

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

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

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

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).
IEEE DOI 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).
IEEE DOI 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

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

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
Change Detection for Remote Sensing Image Level .


Last update:Mar 16, 2024 at 20:36:19