Pulse Coupled Neural Networks for Automatic Urban Change Detection at Very High Spatial Resolution

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-10268-4_109 ◽

2009 ◽

pp. 929-942 ◽

Author(s):

Fabio Pacifici ◽

William J. Emery

Keyword(s):

Neural Networks ◽

Change Detection ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Urban Change ◽

Coupled Neural Networks ◽

Very High Spatial Resolution ◽

Pulse Coupled Neural Networks ◽

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Change Detection Based on Pulse-Coupled Neural Networks and the NMI Feature for High Spatial Resolution Remote Sensing Imagery

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2014.2349937 ◽

2015 ◽

Vol 12 (3) ◽

pp. 537-541 ◽

Author(s):

Yanfei Zhong ◽

Wenfeng Liu ◽

Ji Zhao ◽

Liangpei Zhang

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Change Detection ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Imagery ◽

Coupled Neural Networks ◽

Pulse Coupled Neural Networks

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Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2009.10.002 ◽

2010 ◽

Vol 65 (1) ◽

pp. 143-153 ◽

Author(s):

Mourad Bouziani ◽

Kalifa Goïta ◽

Dong-Chen He

Keyword(s):

Change Detection ◽

Urban Environment ◽

Prior Knowledge ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Very High Spatial Resolution ◽

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A Contrario Comparison of Local Descriptors for Change Detection in Very High Spatial Resolution Satellite Images of Urban Areas

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2018.2888985 ◽

2019 ◽

Vol 57 (6) ◽

pp. 3904-3918 ◽

Author(s):

Gang Liu ◽

Yann Gousseau ◽

Florence Tupin

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

Urban Areas ◽

Satellite Images ◽

High Spatial Resolution ◽

Local Descriptors ◽

Very High Spatial Resolution ◽

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Unsupervised Change Detection for Very High-spatial Resolution Satellite Imagery by Using Object-based IR-MAD Algorithm

Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography ◽

10.7848/ksgpc.2015.33.4.297 ◽

2015 ◽

Vol 33 (4) ◽

pp. 297-304 ◽

Author(s):

Choi Jaewan

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

Satellite Imagery ◽

High Spatial Resolution ◽

Object Based ◽

Very High Spatial Resolution ◽

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Building Change Detection using Object-Oriented LBP Feature Map in Very High Spatial Resolution Imagery

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) ◽

10.1109/multi-temp.2019.8866919 ◽

2019 ◽

Author(s):

Lan Zhang ◽

Bo Zhong ◽

Aixia Yang

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Object Oriented ◽

Feature Map ◽

Very High Spatial Resolution ◽

Very High ◽

High Spatial Resolution Imagery

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Unsupervised change detection frameworks for very high spatial resolution images

2010 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2010.5650560 ◽

2010 ◽

Author(s):

F. Pacifici ◽

C. Padwick ◽

G. Marchisio

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Very High Spatial Resolution ◽

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Change detection in urban areas using very high spatial resolution satellite images: case study in Brussels

10.1117/12.627793 ◽

2005 ◽

Author(s):

Evelyne Frauman ◽

Eléonore Wolff

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

Urban Areas ◽

Satellite Images ◽

High Spatial Resolution ◽

Very High Spatial Resolution ◽

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Extraction of a Specific Land-Cover Class from Very High Spatial Resolution Imagery Using Positive and Unlabeled Learning with Convolutional Neural Networks

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8898974 ◽

2019 ◽

Author(s):

Khelifa Djerriri ◽

Moussa Sofiane Karoui ◽

Reda Adjoudj

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Land Cover Class ◽

Very High Spatial Resolution ◽

Positive And Unlabeled Learning ◽

Cover Class ◽

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Object based change detection of Central Asian Tugai vegetation with very high spatial resolution satellite imagery

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2014.03.004 ◽

2014 ◽

Vol 31 ◽

pp. 110-121 ◽

Author(s):

Philipp Gärtner ◽

Michael Förster ◽

Alishir Kurban ◽

Birgit Kleinschmit

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

Satellite Imagery ◽

High Spatial Resolution ◽

Central Asian ◽

Object Based ◽

Very High Spatial Resolution ◽

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Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

Water ◽

10.3390/w10020001 ◽

2018 ◽

Vol 10 (2) ◽

pp. 1 ◽

Author(s):

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Keyword(s):

Change Detection ◽

Land Cover Change ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Aerial Images ◽

Very High Spatial Resolution ◽

Very High ◽

Land Cover Change Detection

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