Multi-Modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 Data Fusion Contest

被引：146

作者：

Longbotham, Nathan ^{[11
]}

Pacifici, Fabio ^{[10
]}

Glenn, Taylor ^{[9
]}

Zare, Alina ^{[8
]}

Volpi, Michele ^{[7
]}

Tuia, Devis ^{[6
]}

Christophe, Emmanuel ^{[5
]}

Michel, Julien ^{[4
]}

Inglada, Jordi ^{[3
]}

Chanussot, Jocelyn ^{[1
]}

Du, Qian ^{[2
]}

机构：

[1] Grenoble Inst Technol, GIPSA Lab, F-38402 Grenoble, France

[2] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA

[3] Ctr Natl Etud Spatiales, F-34401 Toulouse 09, France

[4] Commun & Syst, F-31506 Toulouse, France

[5] Natl Univ Singapore, CRISP, Singapore 119260, Singapore

[6] Ecole Polytech Fed Lausanne, LASIG Lab, CH-1015 Lausanne, Switzerland

[7] Univ Lausanne, Inst Geomat & Anal Risk, CH-1015 Lausanne, Switzerland

[8] Univ Missouri, Dept Elect & Comp Engn, Columbia, MO 65211 USA

[9] Univ Florida, Dept Comp & Informat Sci & Engn, Gainesville, FL 32601 USA

[10] DigitalGlobe Inc, Longmont, CO 80504 USA

[11] Univ Colorado, Dept Aerosp Engn Sci, Boulder, CO 80302 USA

来源：

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2012年 / 5卷 / 01期

基金：

瑞士国家科学基金会;

关键词：

Change detection; data fusion; decision fusion; flood detection; high spatial resolution; optical; synthetic aperture radar; UNSUPERVISED CHANGE DETECTION; SUPPORT VECTOR MACHINES; NEURAL-NETWORKS; MULTISPECTRAL IMAGES; CLASSIFICATION; SAR; SEGMENTATION;

D O I：

10.1109/JSTARS.2011.2179638

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

引用

页码：331 / 342

页数：12

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