22.3.2.1 Site Model Change Detection, Map Update

Chapter Contents (Back)
Remote Sensing. Registration. Change Detection. Map Update. Aerial Image Analysis. See also Change Detection -- Image Level. See also Building Change Detection.

Ridd, M.K., Liu, J.J.,
A Comparison of Four Algorithms for Change Detection in an Urban Environment,
RSE(63), No. 2, February 1998, pp. 95-100. 9801
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Sarkar, S.[Sudeep], Boyer, K.L.[Kim L.],
Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors,
CVIU(71), No. 1, July 1998, pp. 110-136.
DOI Link BibRef 9807
Earlier: CVPR96(478-483).
IEEE DOI Change Detection. Analysis of construction sites. BibRef

Metternicht, G.[Graciela],
Change detection assessment using fuzzy sets and remotely sensed data: an application of topographic map revision,
PandRS(54), No. 4, September 1999, pp. 221-233. 9911
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Huertas, A.[Andres], and Nevatia, R.[Ramakant],
Detecting Changes in Aerial Views of Man-Made Structures,
IVC(18), No. 8, 15 May 2000, pp. 583-596.
Elsevier DOI 0003
BibRef USC Computer Vision BibRef
Earlier: ICCV98(73-80).
IEEE DOI
PDF File. BibRef
And: Radius97(319-334). BibRef
And: ARPA96(381-388). Change Detection. BibRef

Bejanin, M., Huertas, A., Medioni, G., and Nevatia, R.,
Model Validation for Change Detection,
WACV94(160-167).
IEEE Abstract. BibRef 9400 USC Computer Vision BibRef
And: ARPA94(I:287-294). BibRef USC Computer Vision Change Detection. BibRef

Huertas, A., Bejanin, M., and Nevatia, R.,
Model Registration and Validation,
Ascona95(33-42). BibRef 9500 USC Computer Vision BibRef

Chellappa, R., Burlina, P., Lin, C.L., Zhang, X., Davis, L.S., Rosenfeld, A.,
Site Model Based Image Registration and Change Detection,
UMD--Radius, June 1999.
PS File. BibRef 9906

Chellappa, R., Zheng, Q., Davis, L.S., DeMenthon, D.F., and Rosenfeld, A.,
Site-Model-Based Change Detection and Image Registration,
DARPA93(205-216). Change Detection, Differencing. Register the image by warping it and substract the image. BibRef 9300

Chellappa, R., Zhang, X.P.[Xiao-Peng], Philippe, B.,
Automatic Image-to-Site Model Registration,
ICASSP96(XX) Ctr. for Automation Rsch. University of Maryland. BibRef 9603

Smits, P.C., Myers, W.L.,
Echelon Approach to Characterize and Understand Spatial Structures of Change in Multitemporal Remote Sensing Imagery,
GeoRS(38), No. 5, September 2000, pp. 2299-2309.
IEEE Top Reference. 0010
BibRef

Agouris, P.[Peggy], Beard, K.[Kate], Mountrakis, G.[Georgios], Stefanidis, A.[Anthony],
Capturing and Modeling Geographic Object Change: A SpatioTemporal Gazetteer Framework,
PandRS(55), No. 10, October 2000, pp. 1241-1250. The framework links an image repository with changes, to instances of geographic entities. 0010
BibRef

Hazel, G.G.,
Object-level change detection in spectral imagery,
GeoRS(39), No. 3, March 2001, pp. 553-561.
IEEE Top Reference. 0104
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Yamamoto, T., Hanaizumi, H., Chino, S.,
A change detection method for remotely sensed multispectral and multitemporal images using 3-D segmentation,
GeoRS(39), No. 5, May 2001, pp. 976-985.
IEEE Top Reference. 0106
BibRef

Bruzzone, L., Fernandez-Prieto, D.,
An adaptive semiparametric and context-based approach to unsupervised change detection multitemporal remote-sensing images,
IP(11), No. 4, April 2002, pp. 452-466.
IEEE DOI 0205
See also iterative approach to partially supervised classification problems, An. See also minimum-cost thresholding technique for unsupervised change detection, A. BibRef

Bruzzone, L., Fernandez-Prieto, D.,
An adaptive parcel-based technique for unsupervised change detection,
JRS(21), No. 4, March 2000, pp. 817. Uses the neighborhood to reduce noise. 0002
BibRef

Bruzzone, L., Fernández-Prieto, D.[Diego],
Automatic Analysis of the Difference Image for Unsupervised Change Detection,
GeoRS(38), No. 3, May 2000, pp. 1171-1182.
IEEE Top Reference. 0006
BibRef

Bruzzone, L.[Lorenzo], Fernández-Prieto, D.[Diego],
A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images,
PRL(23), No. 9, July 2002, pp. 1063-1071.
Elsevier DOI 0205
BibRef

Liu, X., Lathrop, Jr., R.G.,
Urban change detection based on an artificial neural network,
JRS(23), No. 12, June 2002, pp. 2513-2518. 0208
BibRef

Leclerc, Y.G.[Yvan G.], Luong, Q.T.[Q. Tuan], Fua, P.,
Self-Consistency and MDL: A Paradigm for Evaluating Point-Correspondence Algorithms, and Its Application to Detecting Changes in Surface Elevation,
IJCV(51), No. 1, January 2003, pp. 63-83.
DOI Link 0211
BibRef

Leclerc, Y.G., Luong, Q.T., and Fua, P.V.,
A framework for detecting changes in terrain,
DARPA98(621-630).
PDF File. BibRef 9800

Leclerc, Y.G.[Yvan G.], Luong, Q.T.[Q. Tuan], Fua, P.V.[Pascal V.], Miyajima, K.[Koji],
Detecting Changes in 3-D Shape using Self-Consistency,
CVPR00(I: 395-402).
IEEE DOI
PDF File. 0005
Applies to DEM representation BibRef

Leclerc, Y.G.[Yvan G.],
Continuous Terrain Modeling from Image Sequences with Applications to Change Detection,
DARPA97(431-436).
PDF File. BibRef 9700

Couteron, P.[Pierre],
Quantifying change in patterned semi-arid vegetation by Fourier analysis of digitized aerial photographs,
JRS(23), No. 17, September 2002, pp. 3407-3425.
WWW Link. 0211
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Seto, K.C.[Karen C.], Liu, W.G.[Wei-Guo],
Comparing ARTMAP Neural Network with the Maximum-Likelihood Classifier for Detecting Urban Change,
PhEngRS(69), No. 9, September 2003, pp. 981-990. An ARTMAP neural network was used to identify urban land-use change with different class resolutions; it generated more accurate results when compared to a Bayesian maximum-likelihood classifier.
WWW Link. 0309
BibRef

Knudsen, T.[Thomas], Olsen, B.P.[Brian P.],
Automated Change Detection for Updates of Digital Map Databases,
PhEngRS(69), No. 11, November 2003, pp. 1289-1298. The change detection algorithm uses vector and spectral data as input to an unsupervised spectral classification method which controls a subsequent Mahalanobis classification step.
WWW Link. 0401
BibRef

Peerbocus, M.A., Bauzer Medeiros, C., Jomier, G., Voisard, A.,
A System for Change Documentation Based on a Spatiotemporal Database,
GeoInfo(8), No. 2, June 2004, pp. 173-204.
DOI Link 0403
BibRef

Walter, V.[Volker],
Object-based classification of remote sensing data for change detection,
PandRS(58), No. 3-4, January 2004, pp. 225-238.
Elsevier DOI 0411
BibRef

Lacroix, V., Idrissa, M., Hincq, A., Bruynseels, H., Swartenbroekx, O.,
Detecting urbanization changes using SPOT5,
PRL(27), No. 4, March 2006, pp. 226-233.
Elsevier DOI 0602
Cartography; Change detection; SPOT5; Built-up area detection BibRef

Lambin, E.F., Linderman, M.,
Time Series of Remote Sensing Data for Land Change Science,
GeoRS(44), No. 7, Part 1, July 2006, pp. 1926-1928.
IEEE DOI 0606
BibRef

Gamba, P., Dell'Acqua, F., Lisini, G.,
Change Detection of Multitemporal SAR Data in Urban Areas Combining Feature-Based and Pixel-Based Techniques,
GeoRS(44), No. 10, October 2006, pp. 2820-2827.
IEEE DOI 0609
BibRef

Dell'Acqua, F., Gamba, P., Lisini, G.,
A Semi-Automatic High Resolution SAR Data Interpretation Procedure,
PIA07(19).
PDF File. 0711
BibRef

Holland, D.A., Boyd, D.S., Marshall, P.,
Updating topographic mapping in Great Britain using imagery from high-resolution satellite sensors,
PandRS(60), No. 3, May 2006, pp. 212-223.
Elsevier DOI 0610
cartography; change detection; land cover; IKONOS; QuickBird BibRef

Molinier, M.[Matthieu], Laaksonen, J.T.[Jorma T.], Hame, T.[Tuomas],
Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps,
GeoRS(45), No. 4, April 2007, pp. 861-874.
IEEE DOI 0704
See also PicSOM: Content-Based Image Retrieval with Self-Organizing Maps. BibRef

Chini, M., Pacifici, F., Emery, W.J., Pierdicca, N., Del Frate, F.,
Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats,
GeoRS(46), No. 6, June 2008, pp. 1812-1821.
IEEE DOI 0711
See also Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines. BibRef

Pacifici, F.[Fabio], Emery, W.J.[William J.],
Pulse Coupled Neural Networks for Automatic Urban Change Detection at Very High Spatial Resolution,
CIARP09(929-942).
Springer DOI 0911
See also Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines. BibRef

Camps-Valls, G.[Gustavo], Gomez-Chova, L., Munoz-Mari, J., Rojo-Alvarez, J.L., Martinez-Ramon, M.,
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection,
GeoRS(46), No. 6, June 2008, pp. 1822-1835.
IEEE DOI 0711
BibRef

Tuia, D.[Devis], Marcos, D.[Diego], Camps-Valls, G.[Gustau],
Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization,
PandRS(120), No. 1, 2016, pp. 1-12.
Elsevier DOI 1610
Feature extraction BibRef

Leiva-Murillo, J.M., Gomez-Chova, L., Camps-Valls, G.,
Multitask Remote Sensing Data Classification,
GeoRS(51), No. 1, January 2013, pp. 151-161.
IEEE DOI 1301
BibRef

Potere, D.[David], Feierabend, N.[Neal], Strahler, A.H.[Alan H.], Bright, E.E.[Eddie E.],
Wal-mart from Space: A New Source for Land Cover Change Validation,
PhEngRS(74), No. 7, July 2008, pp. 913-920.
WWW Link. 0804
Using a set of Wal-Mart store positions and opening dates to validate portions of three land-cover change-related products: a forest disturbance map based on Landsat GeoCover imagery and two MODIS vegetation index time series. BibRef

Wilkinson, D.W., Parker, R.C., Evans, D.L.,
Change Detection Techniques for Use in a Statewide Forest Inventory Program,
PhEngRS(74), No. 7, July 2008, pp. 893-902.
WWW Link. 0804
Analysis of modifi ed Change Vector Analysis (mCVA) and Simultaneous Image Differencing (SID) techniques for largescale forest change in Mississippi. BibRef

Pape, A.D.[Alysha D.], Franklin, S.E.[Steven E.],
Modis-based Change Detection for Grizzly Bear Habitat Mapping in Alberta,
PhEngRS(74), No. 8, August 2008, pp. 973-986.
WWW Link. 0804
Multiple spatial resolution, polygon-based, image change detection in Boreal forests for Grizzly Bear Management. BibRef

Liao, M.S.[Ming-Sheng], Jiang, L.M.[Li-Ming], Lin, H.[Hui], Huang, B.[Bo], Gong, J.Y.[Jian-Ya],
Urban Change Detection Based on Coherence and Intensity Characteristics of Sar Imagery,
PhEngRS(74), No. 8, August 2008, pp. 999-1066.
WWW Link. 0804
An unsupervised approach combining coherence and intensity characteristics of SAR imagery to detect and map landcover changes in an urban area. BibRef

Durieux, L.[Laurent], Lagabrielle, E.[Erwann], Nelson, A.[Andrew],
A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data,
PandRS(63), No. 4, July 2008, pp. 399-408.
Elsevier DOI 0804
Spot 5; Reunion Island; Integrated urban sprawl management; Object-based image analysis BibRef

Bouziani, M.[Mourad], Goita, K.[Kalifa], He, D.C.[Dong-Chen],
Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge,
PandRS(65), No. 1, January 2010, pp. 143-153.
Elsevier DOI 1001
Change detection; Urban; QuickBird; Ikonos; Knowledge base BibRef

Li, X.[Xia], Yeh, A.G.O.[Anthony Gar-On], Qian, J.P.[Jun-Ping], Ai, B.[Bin], Qi, Z.X.[Zhi-Xin],
A Matching Algorithm for Detecting Land Use Changes Using Case-Based Reasoning,
PhEngRS(75), No. 11, November 2009, pp. 1319-1333.
WWW Link. 1001
A matching algorithm to identify the temporal positions and the kind of changes by integrating object-oriented analysis and case-based reasoning for Multi-temporal SAR Images. BibRef

Lu, D.S.[Deng-Sheng], Moran, E.[Emilio], Hetrick, S.[Scott],
Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier,
PandRS(66), No. 3, May 2011, pp. 298-306.
Elsevier DOI 1103
Impervious surfaces; Urban-rural frontier; Landsat; QuickBird; Regression analysis BibRef

Jiang, J.X.[Ji-Xiang], Worboys, M.[Michael], Nittel, S.[Silvia],
Qualitative change detection using sensor networks based on connectivity information,
GeoInfo(15), No. 2, April 2011, pp. 305-328.
WWW Link. 1103
BibRef

Crispell, D., Mundy, J., Taubin, G.,
A Variable-Resolution Probabilistic Three-Dimensional Model for Change Detection,
GeoRS(50), No. 2, February 2012, pp. 489-500.
IEEE DOI 1201
BibRef

Hebel, M.[Marcus], Stilla, U.[Uwe],
Simultaneous Calibration of ALS Systems and Alignment of Multiview LiDAR Scans of Urban Areas,
GeoRS(50), No. 6, June 2012, pp. 2364-2379.
IEEE DOI 1205
BibRef

Hebel, M.[Marcus], Arens, M.[Michael], Stilla, U.[Uwe],
Change Detection in Urban Areas by Direct Comparison of Multi-view and Multi-temporal ALS Data,
PIA11(185-196).
Springer DOI 1110
BibRef

Chaabouni-Chouayakh, H.[Houda], Reinartz, P.[Peter],
Towards Automatic 3D Change Detection inside Urban Areas by Combining Height and Shape Information,
PFG(2011), No. 4, 2011, pp. 205-217.
WWW Link. 1211
BibRef

Chaabouni-Chouayakh, H.[Houda], Krauss, T.[Thomas], d'Angelo, P.[Pablo], Reinartz, P.[Peter],
3D Change Detection inside Urban Areas using different Digital Surface Models,
PCVIA10(B:86).
PDF File. 1009
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Listner, C.[Clemens], Niemeyer, I.[Irmgard],
Object-based Change Detection,
PFG(2011), No. 4, 2011, pp. 233-245.
WWW Link. 1211
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Huh, Y.[Yong], Yang, S.[Sungchul], Ga, C.[Chillo], Yu, K.[Kiyun], Shi, W.Z.[Wen-Zhong],
Line segment confidence region-based string matching method for map conflation,
PandRS(78), No. 1, April 2013, pp. 69-84.
Elsevier DOI 1304
Map conflation; Spatial uncertainty; Confidence region of a line segment; String matching; Corresponding point pair BibRef

Hussain, M.[Masroor], Chen, D.M.[Dong-Mei], Cheng, A.[Angela], Wei, H.[Hui], Stanley, D.[David],
Change detection from remotely sensed images: From pixel-based to object-based approaches,
PandRS(80), No. 1, June 2013, pp. 91-106.
Elsevier DOI 1305
Remote sensing; Change detection; Pixel-based; Object-based; Spatial-data-mining BibRef

Hwang, J.S.[Jin-Sang], Yun, H.S.[Hong-Sik], Jeong, T.J.[Tae-Jun], Suh, Y.[Yong_Cheol], Huang, H.[He],
Frequent Unscheduled Updates of the National Base Map Using the Land-Based Mobile Mapping System,
RS(5), No. 5, 2013, pp. 2513-2533.
DOI Link 1307
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Lubitz, C.[Christin], Motagh, M.[Mahdi], Wetzel, H.U.[Hans-Ulrich], Kaufmann, H.[Hermann],
Remarkable Urban Uplift in Staufen im Breisgau, Germany: Observations from TerraSAR-X InSAR and Leveling from 2008 to 2011,
RS(5), No. 6, 2013, pp. 3082-3100.
DOI Link 1307
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Touya, G.[Guillaume], Coupé, A.[Adeline], Le Jollec, J.[Jérémie], Dorie, O.[Olivier], Fuchs, F.[Frank],
Conflation Optimized by Least Squares to Maintain Geographic Shapes,
IJGI(2), No. 3, 2013, pp. 621-644.
DOI Link 1307
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Li, H.F.[Hai-Feng], Wu, B.[Bo],
Adaptive geo-information processing service evolution: Reuse and local modification method,
PandRS(83), No. 1, 2013, pp. 165-183.
Elsevier DOI 1308
Geography information services BibRef

Jaud, M.[Marion], Rouveure, R.[Raphaël], Faure, P.[Patrice], Monod, M.O.[Marie-Odile],
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Elsevier DOI 1309
Radar mapping See also Method for orthorectification of terrestrial radar maps. BibRef

Rössmann, H.[Heiner], Peyker, J.[Joachim], Völker, A.[Andreas], Klink, A.[Adrian],
Einsatz von Change-Detection-Methoden bei der Fortführung von Versiegelungs- und Gebäudedatenbeständen,
PFG(2013), No. 5, 2013, pp. 447-458.
DOI Link 1310
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Wang, J.[Jinhu], González-Jorge, H.[Higinio], Lindenbergh, R.[Roderik], Arias-Sánchez, P.[Pedro], Menenti, M.[Massimo],
Automatic Estimation of Excavation Volume from Laser Mobile Mapping Data for Mountain Road Widening,
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Paris, P.[Paul], Mitasova, H.[Helena],
Barrier Island Dynamics Using Mass Center Analysis: A New Way to Detect and Track Large-Scale Change,
IJGI(3), No. 1, 2014, pp. 49-65.
DOI Link 1402
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Vassilakis, E.[Emmanuel], Papadopoulou-Vrynioti, K.[Kyriaki],
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IJGI(3), No. 1, 2014, pp. 18-28.
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Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing,
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Qin, R.J.[Rong-Jun],
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Fraser, R.H.[Robert H.], Olthof, I.[Ian], Kokelj, S.V.[Steven V.], Lantz, T.C.[Trevor C.], Lacelle, D.[Denis], Brooker, A.[Alexander], Wolfe, S.[Stephen], Schwarz, S.[Steve],
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization,
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Olthof, I.[Ian], Fraser, R.H.[Robert H.],
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification,
RS(6), No. 11, 2014, pp. 11558-11578.
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Ahmed, M.[Mahmuda], Karagiorgou, S.[Sophia], Pfoser, D.[Dieter], Wenk, C.[Carola],
A comparison and evaluation of map construction algorithms using vehicle tracking data,
GeoInfo(19), No. 3, July 2015, pp. 601-632.
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Liu, C.Y.[Chang-Yong], Xiong, L.[Lian], Hu, X.Y.[Xiang-Yun], Shan, J.[Jie],
A Progressive Buffering Method for Road Map Update Using OpenStreetMap Data,
IJGI(4), No. 3, 2015, pp. 1246.
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Maurer, J.[Joshua], Rupper, S.[Summer],
Tapping into the Hexagon spy imagery database: A new automated pipeline for geomorphic change detection,
PandRS(108), No. 1, 2015, pp. 113-127.
Elsevier DOI 1511
Stereo imagery BibRef

Dorn, H.[Helen], Törnros, T.[Tobias], Zipf, A.[Alexander],
Quality Evaluation of VGI Using Authoritative Data: A Comparison with Land Use Data in Southern Germany,
IJGI(4), No. 3, 2015, pp. 1657.
DOI Link 1511
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Wen, D.[Dawei], Huang, X.[Xin], Zhang, L.P.[Liang-Pei], Benediktsson, J.A.,
A Novel Automatic Change Detection Method for Urban High-Resolution Remotely Sensed Imagery Based on Multi-Index Scene Representation,
GeoRS(54), No. 1, January 2016, pp. 609-625.
IEEE DOI 1601
feature extraction BibRef

Zhang, P.Z.[Pu-Zhao], Gong, M.[Maoguo], Su, L.Z.[Lin-Zhi], Liu, J.[Jia], Li, Z.Z.[Zhi-Zhou],
Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images,
PandRS(116), No. 1, 2016, pp. 24-41.
Elsevier DOI 1604
Change detection BibRef

Gong, M.[Maoguo], Zhan, T.[Tao], Zhang, P.Z.[Pu-Zhao], Miao, Q.G.[Qi-Guang],
Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images,
GeoRS(55), No. 5, May 2017, pp. 2658-2673.
IEEE DOI 1705
feature extraction, geophysical image processing, land cover, neural nets, remote sensing, bitemporal multispectral, change detection, change feature extraction, hierarchical difference representation learning, high resolution remotely sensed imagery, land cover transition, multispectral remote sensing images, neural networks, preclassification map, satellite sensors, semantic difference, superpixel based difference representation learning, Feature extraction, Image analysis, Image resolution, Image segmentation, Neural networks, Remote sensing, Robustness, Change detection, difference representation learning, multispectral images, neural network, superpixel, segmentation BibRef

Zhang, P.Z.[Pu-Zhao], Lv, Z., Zhang, D., Chen, J.,
A Shape Similarity Based Change Detection Approach of Multi-resolution Remote Sensing Images,
AnnalsPRS(I-7), No. 2012, pp. 263-266.
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Zhang, X.C.[Xin-Chang], Guo, T.S.[Tai-Sheng], Huang, J.F.[Jian-Feng], Xin, Q.C.[Qin-Chuan],
Propagating Updates of Residential Areas in Multi-Representation Databases Using Constrained Delaunay Triangulations,
IJGI(5), No. 6, 2016, pp. 80.
DOI Link 1608
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Chen, Q.A.[Qi-Ang], Chen, Y.H.[Yun-Hao],
Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis,
RS(8), No. 7, 2016, pp. 549.
DOI Link 1608
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Chen, K.T.[Kuan-Ting], Wang, F.E.[Fu-En], Lin, J.T.[Juan-Ting], Chan, F.H.[Fu-Hsiang], Sun, M.[Min],
The World Is Changing: Finding Changes on the Street,
CVTSV16(I: 420-435).
Springer DOI 1704
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Degol, J.[Joseph], Golparvar-Fard, M.[Mani], Hoiem, D.[Derek],
Geometry-Informed Material Recognition,
CVPR16(1554-1562)
IEEE DOI 1612
3D to assist 2D in material recogniton. i.e. construction site. BibRef

Floros, G., Dimopoulou, E.,
Investigating the Enrichment of a 3D City Model with Various CITYGML Modules,
GeoInfo16(3-9).
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Floros, G., Solou, D., Pispidikis, I., Dimopoulou, E.,
A Roadmap for Generating Semantically Enriched Building Models According to CITYGML Model Via Two Different Methodologies,
GeoInfo16(23-32).
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Jia, Y.H.[Yong-Hong], Zhou, M.[Mingting], Jinshan, Y.[Ye],
Object-oriented Change Detection Based On Multi-scale Approach,
ISPRS16(B7: 517-522).
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Park, J.G., Harada, I., Kwak, Y.,
Object-based Classification And Change Detection Of Hokkaido, Japan,
ISPRS16(B8: 1003-1007).
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Alrajhi, M.[Muhamad], Janjua, K.S.[Khurram Shahzad], Khan, M.A.[Mohammad Afroz], Alobeid, A.[Abdalla],
Updating Maps Using High Resolution Satellite Imagery,
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Keinan, E., Felus, Y.A., Tal, Y., Zilberstien, O., Elihai, Y.,
Updating National Topographic Data Base Using Change Detection Methods,
ISPRS16(B7: 529-536).
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Cantemir, A., Visan, A., Parvulescu, N., Dogaru, M.,
The Use Of Multiple Data Sources In The Process Of Topographic Maps Updating,
ISPRS16(B4: 19-24).
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Tuttas, S., Braun, A., Borrmann, A., Stilla, U.,
Evaluation Of Acquisition Strategies For Image-based Construction Site Monitoring,
ISPRS16(B5: 733-740).
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Matikainen, L.[Leena], Hyyppä, J.[Juha], Litkey, P.[Paula],
Multispectral Airborne Laser Scanning For Automated Map Updating,
ISPRS16(B3: 323-330).
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Xu, Y., Tuttas, S., Stilla, U.,
Segmentation of 3D outdoor scenes using hierarchical clustering structure and perceptual grouping laws,
PRRS16(1-6)
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image segmentation BibRef

Xu, Y., Tuttas, S., Heogner, L., Stilla, U.,
Classification Of Photogrammetric Point Clouds Of Scaffolds For Construction Site Monitoring Using Subspace Clustering And Pca,
ISPRS16(B3: 725-732).
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Lee, L., Smith, B., Chen, T.,
Fine-grain uncommon object detection from satellite images,
AIPR15(1-6)
IEEE DOI 1605
geophysical image processing BibRef

Yang, C.H., Soergel, U.,
Change Detection Based on Persistent Scatterer Interferometry: Case Study of Monitoring an Urban Area,
CMRT15(123-130).
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Vakalopoulou, M.[Maria], Karatzalos, K.[Konstantinos], Komodakis, N.[Nikos], Paragios, N.[Nikos],
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Chapter on Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR continues in
Building Change Detection .


Last update:Sep 25, 2017 at 16:36:46