Border detection on remote sensing satellite data using self-organizing maps

被引：0

作者：

Marques, NC ^{[1
]}

Chen, N ^{[1
]}

机构：

[1] Univ Nova Lisboa, Fac Ciencias & Tecnol, Dept Informat, CENTRIA, P-2829516 Caparica, Portugal

来源：

PROGRESS IN ARTIFICIAL INTELLIGENCE-B | 2003年 / 2902卷

关键词：

remote sensing satellite data; border detection; self-organizing map (SOM); clustering; gradient edge detector;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a new approach to Mediterranean Water Eddy border detection is proposed. Kohonen self-organizing maps (SOM) are used as data mining tools to cluster image pixels through an unsupervised process. The clusters are visualized on the SOM internal map. From the visualization, the borders can be detected through an interactive way. As a result, interesting patterns are visible on the images. The proposed SOM approach is tested on Atlantic Ocean satellite data and compared with conventional gradient edge detectors.

引用

页码：294 / 307

页数：14

共 50 条

[1] Border detection on remote sensing satellite data using self-organizing maps
Marques, Nuno C.
Chen, Ning
[J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2003, 2902 : 294 - 307
[2] Classification of remote sensing data by multistage self-organizing maps with rejection schemes
Lee, J
Ersoy, OK
[J]. RAST 2005: Proceedings of the 2nd International Conference on Recent Advances in Space Technologies, 2005, : 534 - 539
[3] Concurrent Self-Organizing Maps for Supervised/Unsupervised Change Detection in Remote Sensing Images
Neagoe, Victor-Emil
Stoica, Radu-Mihai
Ciurea, Alexandru-Ioan
Bruzzone, Lorenzo
Bovolo, Francesca
[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (08) : 3525 - 3533
[4] Novelty detection using Self-Organizing Maps
Ypma, A
Duin, RPW
[J]. PROGRESS IN CONNECTIONIST-BASED INFORMATION SYSTEMS, VOLS 1 AND 2, 1998, : 1322 - 1325
[5] Using self-organizing maps to identify patterns in satellite imagery
Richardson, AJ
Risien, C
Shillington, FA
[J]. PROGRESS IN OCEANOGRAPHY, 2003, 59 (2-3) : 223 - 239
[6] Phytoplankton Diversity in the Mediterranean Sea From Satellite Data Using Self-Organizing Maps
El Hourany, Roy
Saab, Marie Abboud-Abi
Faour, Ghaleb
Mejia, Carlos
Crepon, Michel
Thiria, Sylvie
[J]. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2019, 124 (08) : 5827 - 5843
[7] Intrusion detection using Emergent Self-Organizing Maps
Mitrokotsa, Aikaterini
Douligeris, Christos
[J]. ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 3955 : 559 - 562
[8] Intrusion Detection System using Self-Organizing Maps
Alsulaiman, Mansour M.
Alyahya, Aasem N.
Alkharboush, Raed A.
Alghafis, Nasser S.
[J]. NSS: 2009 3RD INTERNATIONAL CONFERENCE ON NETWORK AND SYSTEM SECURITY, 2009, : 397 - +
[9] Meteorological data analysis using self-organizing maps
Tambouratzis, Tatiana
Tambouratzis, George
[J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2008, 23 (06) : 735 - 759
[10] Visualization of Agriculture Data Using Self-Organizing Maps
Russ, Georg
Kruse, Rudolf
Schneider, Martin
Wagner, Peter
[J]. APPLICATIONS AND INNOVATIONS IN INTELLIGENT SYSTEMS XVI, 2009, : 47 - +

← 1 2 3 4 5 →