12.1.5.1 Change Detection for Remote Sensing Image Level

Chapter Contents (Back)
Change Detection. Remote Sensing.
See also Land Cover Change Analysis, Remote Sensing Change Analysis, Temporal Analysis.

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

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

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

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

Liu, Z., Li, G., Mercier, G., He, Y., Pan, Q.,
Change Detection in Heterogenous Remote Sensing Images via Homogeneous Pixel Transformation,
IP(27), No. 4, April 2018, pp. 1822-1834.
IEEE DOI 1802
geophysical image processing, pattern clustering, remote sensing, HPT, K, backward transformation, belief functions theory, remote sensing 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.
Elsevier DOI 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

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

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

Bergamasco, L., Saha, S., Bovolo, F., Bruzzone, L.,
An Explainable Convolutional Autoencoder Model for Unsupervised Change Detection,
ISPRS20(B2:1513-1519).
DOI Link 2012
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
See also Novel Change Detection Method for Multitemporal Hyperspectral Images Based on Binary Hyperspectral Change Vectors, A. 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

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

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

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

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

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.[Chen], Zhang, L.P.[Liang-Pei], Du, B.[Bo],
Kernel Slow Feature Analysis for Scene Change Detection,
GeoRS(55), No. 4, April 2017, pp. 2367-2384.
IEEE DOI 1704
Bayes methods 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

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

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

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

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

Liu, Q.J.[Qing-Jie], Liu, L.N.[Li-Ning], 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

Touati, R., Mignotte, M.,
An Energy-Based Model Encoding Nonlocal Pairwise Pixel Interactions for Multisensor Change Detection,
GeoRS(56), No. 2, February 2018, pp. 1046-1058.
IEEE DOI 1802
Estimation, Image sensors, Optical sensors, Remote sensing, Robustness, Synthetic aperture radar, Change detection (CD), pairwise pixel interactions BibRef

Mignotte, M.[Max],
A Fractal Projection and Markovian Segmentation-Based Approach for Multimodal Change Detection,
GeoRS(58), No. 11, November 2020, pp. 8046-8058.
IEEE DOI 2011
Fractals, Satellites, Optical sensors, Image segmentation, Image sensors, Change detection, contractive mapping, multisource BibRef

Zanetti, M., Bruzzone, L.,
A Theoretical Framework for Change Detection Based on a Compound Multiclass Statistical Model of the Difference Image,
GeoRS(56), No. 2, February 2018, pp. 1129-1143.
IEEE DOI 1802
Compounds, Data models, Radiometry, Remote sensing, Satellites, Sensors, Statistical distributions, Change detection (CD), multispectral (MS) multitemporal images BibRef

Ferraris, V., Dobigeon, N., Wei, Q., Chabert, M.,
Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: A Fusion-Based Approach,
GeoRS(56), No. 3, March 2018, pp. 1566-1578.
IEEE DOI 1804
hyperspectral imaging, image fusion, image resolution, remote sensing, change detection, fusion-based approach, multispectral (MS) imagery BibRef

Fang, B.[Bo], Pan, L.[Li], Kou, R.[Rong],
Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Touati, R., Mignotte, M., Dahmane, M.,
Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise-Based Markov Random Field Model,
IP(29), No. 1, 2020, pp. 757-767.
IEEE DOI 1910
Imaging, Remote sensing, Estimation, Visualization, Discrete cosine transforms, Satellites, Bayes methods, unsupervised Markovian segmentation BibRef

Padrón-Hidalgo, J.A., Laparra, V., Longbotham, N., Camps-Valls, G.,
Kernel Anomalous Change Detection for Remote Sensing Imagery,
GeoRS(57), No. 10, October 2019, pp. 7743-7755.
IEEE DOI 1910
geophysical image processing, Hilbert spaces, image resolution, remote sensing, utilize Gaussian distribution, kernel methods BibRef

Du, B., Ru, L., Wu, C., Zhang, L.,
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images,
GeoRS(57), No. 12, December 2019, pp. 9976-9992.
IEEE DOI 1912
Feature extraction, Remote sensing, Change detection algorithms, Detection algorithms, Eigenvalues and eigenfunctions, slow feature analysis (SFA) BibRef

Wang, M.[Moyang], Tan, K.[Kun], Jia, X.P.[Xiu-Ping], Wang, X.[Xue], Chen, Y.[Yu],
A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Gong, M., Duan, Y., Li, H.,
Group Self-Paced Learning With a Time-Varying Regularizer for Unsupervised Change Detection,
GeoRS(58), No. 4, April 2020, pp. 2481-2493.
IEEE DOI 2004
Training, Change detection algorithms, Robustness, Support vector machines, Remote sensing, Feature extraction, self-paced learning (SPL) BibRef

Zhan, T., Gong, M., Jiang, X., Zhang, M.,
Unsupervised Scale-Driven Change Detection With Deep Spatial-Spectral Features for VHR Images,
GeoRS(58), No. 8, August 2020, pp. 5653-5665.
IEEE DOI 2007
Feature extraction, Remote sensing, Data mining, Support vector machines, Land surface, Spatial resolution, support vector machine (SVM) BibRef

Zhang, M.[Min], Shi, W.Z.[Wen-Zhong],
A Feature Difference Convolutional Neural Network-Based Change Detection Method,
GeoRS(58), No. 10, October 2020, pp. 7232-7246.
IEEE DOI 2009
Feature extraction, Training, Sensors, Task analysis, Convolutional neural networks, Spatial resolution, Deep learning, remote sensing (RS) BibRef

Sun, Y.[Yuli], Lei, L.[Lin], Li, X.[Xiao], Sun, H.[Hao], Kuang, G.Y.[Gang-Yao],
Nonlocal patch similarity based heterogeneous remote sensing change detection,
PR(109), 2021, pp. 107598.
Elsevier DOI 2009
Unsupervised change detection, Heterogeneous data, Nonlocal similarity, Graph BibRef

Sun, Y.[Yuli], Lei, L.[Lin], Guan, D.D.[Dong-Dong], Kuang, G.Y.[Gang-Yao],
Iterative Robust Graph for Unsupervised Change Detection of Heterogeneous Remote Sensing Images,
IP(30), 2021, pp. 6277-6291.
IEEE DOI 2107
Radar polarimetry, Optical imaging, Image segmentation, Image sensors, Transforms, Training, Optical sensors, co-segmentation BibRef

Sun, Y.[Yuli], Lei, L.[Lin], Li, X.[Xiao], Tan, X.[Xiang], Kuang, G.Y.[Gang-Yao],
Patch Similarity Graph Matrix-Based Unsupervised Remote Sensing Change Detection With Homogeneous and Heterogeneous Sensors,
GeoRS(59), No. 6, June 2021, pp. 4841-4861.
IEEE DOI 2106
Optical sensors, Remote sensing, Synthetic aperture radar, Optical imaging, Training, Task analysis, Heterogeneous data, unsupervised change detection (CD) BibRef

Lu, N.[Ning], Chen, C.[Can], Shi, W.[Wenbo], Zhang, J.W.[Jun-Wei], Ma, J.F.[Jian-Feng],
Weakly Supervised Change Detection Based on Edge Mapping and SDAE Network in High-Resolution Remote Sensing Images,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Ghaderpour, E.[Ebrahim], Vujadinovic, T.[Tijana],
Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Ru, L., Du, B., Wu, C.,
Multi-Temporal Scene Classification and Scene Change Detection With Correlation Based Fusion,
IP(30), 2021, pp. 1382-1394.
IEEE DOI 2012
Feature extraction, Correlation, Remote sensing, Semantics, Task analysis, Training, Spatial resolution, Change detection, convolutional neural network BibRef

He, P.F.[Peng-Fei], Zhao, X.W.[Xiang-Wei], Shi, Y.[Yuli], Cai, L.P.[Li-Ping],
Unsupervised Change Detection from Remotely Sensed Images Based on Multi-Scale Visual Saliency Coarse-to-Fine Fusion,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Negri, R.G.[Rogério Galante], Frery, A.C.[Alejandro C.], Casaca, W.[Wallace], Azevedo, S.[Samara], Dias, M.A.[Maurício Araújo], Silva, E.A.[Erivaldo Antônio], Alcântara, E.H.[Enner Herênio],
Spectral-Spatial-Aware Unsupervised Change Detection With Stochastic Distances and Support Vector Machines,
GeoRS(59), No. 4, April 2021, pp. 2863-2876.
IEEE DOI 2104
Support vector machines, Remote sensing, Robustness, Stochastic processes, Measurement, Change detection algorithms, unsupervised change detection BibRef

Zheng, Z.[Zhi], Wan, Y.[Yi], Zhang, Y.J.[Yong-Jun], Xiang, S.Z.[Si-Zhe], Peng, D.F.[Dai-Feng], Zhang, B.[Bin],
CLNet: Cross-Layer Convolutional Neural Network for Change Detection in Optical Remote Sensing Imagery,
PandRS(175), 2021, pp. 247-267.
Elsevier DOI 2105
Change detection, Optical remote sensing image, Deep convolutional neural networks, Cross-Layer Block (CLB), UNet BibRef

Hou, X.[Xuan], Bai, Y.P.[Yun-Peng], Li, Y.[Ying], Shang, C.J.[Chang-Jing], Shen, Q.[Qiang],
High-resolution triplet network with dynamic multiscale feature for change detection on satellite images,
PandRS(177), 2021, pp. 103-115.
Elsevier DOI 2106
Change detection, Triplet network, High-resolution images, Dynamic convolution, Remote sensing BibRef

Shao, R.Z.[Rui-Zhe], Du, C.[Chun], Chen, H.[Hao], Li, J.[Jun],
SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef


Zhang, Y., Liu, G., Yuan, Y.,
A Novel Unsupervised Change Detection Approach Based On Spectral Transformation For Multispectral Images,
ICIP20(51-55)
IEEE DOI 2011
Feature extraction, Principal component analysis, Imaging, Remote sensing, Robustness, Transforms, Matrix decomposition, spectral-spatial features BibRef

Ziemann, A.[Amanda], Pitts, T.[Travis],
Exploring feature augmentation as a method for improving panchromatic remote sensing change detection,
SSIAI20(82-85)
IEEE DOI 2009
Find anomalies. feature extraction, geophysical image processing, geophysical signal processing, geophysical techniques, feature augmentation BibRef

Yang, J., Zhou, Y., Cao, Y., Feng, L.,
Heterogeneous image change detection using Deep Canonical Correlation Analysis,
ICPR18(2917-2922)
IEEE DOI 1812
Feature extraction, Sensors, Correlation, Earth, Remote sensing, Image sensors, Training BibRef

Touati, R., Miqnoite, M., Dahmane, M.,
Change Detection in Heterogeneous Remote Sensing Images Based on an Imaging Modality-Invariant MDS Representation,
ICIP18(3998-4002)
IEEE DOI 1809
Satellites, Optical imaging, Histograms, Remote sensing, Feature extraction, Optical sensors BibRef

Touati, R., Mignotte, M., Dahmane, M.,
A new change detector in heterogeneous remote sensing imagery,
IPTA17(1-6)
IEEE DOI 1804
adaptive filters, decision theory, geophysical image processing, image filtering, image segmentation, object detection, Thresholding algorithms BibRef

Tan, H.L., Lu, S.,
Saliency-based change detection for aerial and remote sensing imageries,
ICIP17(3730-3734)
IEEE DOI 1803
geophysical image processing, land use, remote sensing, aerial sensing imageries, captured images, environmental noises, Visualization BibRef

Yang, G.[Gang], Li, H.C.[Heng-Chao], Liu, C.[Chi],
Unsupervised change detection of remote sensing images using superpixel segmentation and variational Gaussian mixture model,
MultiTemp17(1-4)
IEEE DOI 1712
geophysical techniques, remote sensing, GMM, entropy rate superpixel segmentation, Mathematical model 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

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

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

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

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

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

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

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


Last update:Nov 1, 2021 at 09:26:50