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Combining classifiers using Dempster-Shafer evidence theory to improve remote sensing images classification
Classification system and textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. In this work, we propose to fuse the information outputed by 3 well-known classifiers: Support Vector Machines (SVM), Neural Network (NN...
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| creator | Mejdoubi, M. Aboutajdine, D. Kerroum, M. A. Hammouch, A. |
| description | Classification system and textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. In this work, we propose to fuse the information outputed by 3 well-known classifiers: Support Vector Machines (SVM), Neural Network (NN) and Parzen Window (PW). These classifiers were combined according to the Dempster-Shafer theory. The input textural feature used are selected according the GMMFS algorithm. The classification results show that the proposed method gives high performances than those of classifiers separately considered. |
| doi_str_mv | 10.1109/ICMCS.2011.5945724 |
| format | conference_proceeding |
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The classification results show that the proposed method gives high performances than those of classifiers separately considered.</description><subject>Artificial neural networks</subject><subject>Classifier Combination</subject><subject>Dempster-Shafer Theory</subject><subject>GMMFS algorithm</subject><subject>Image color analysis</subject><subject>Pattern recognition</subject><subject>Pixel</subject><subject>Remote sensing</subject><subject>remote sensing images</subject><subject>Support vector machines</subject><subject>Textural Feature</subject><subject>Training</subject><isbn>1612847307</isbn><isbn>9781612847306</isbn><isbn>9781612847313</isbn><isbn>1612847323</isbn><isbn>9781612847320</isbn><isbn>1612847315</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9UMtOwzAQNEJIQMkPwMU_kGB748Q-ogClUhGH9l458bo1NA_ZoVL_ngCFuYx2RjNaDSG3nGWcM32_qF6rVSYY55nUuSxFfkYSXSpecKHyEjick-u_g5WXJInxnU0oCg0KrshH1be173y3pc3exOidxxDpZ_xWHrEd4oghXe2Mw0Dx4C12DdJxh3040rGnvh1Cf0AasO1HpBG7n6RvzRbjf2VjRt93N-TCmX3E5MQzsn5-Wlcv6fJtvqgelqnXbEzrXBvVCMSiBCecBFTKFlYAlyibupayFhrATr5yIHJURQMGgVlnmZAWZuTut9Yj4mYI0y_huDnNA1_Q0lwD</recordid><startdate>201104</startdate><enddate>201104</enddate><creator>Mejdoubi, M.</creator><creator>Aboutajdine, D.</creator><creator>Kerroum, M. 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A.</creatorcontrib><creatorcontrib>Hammouch, A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mejdoubi, M.</au><au>Aboutajdine, D.</au><au>Kerroum, M. A.</au><au>Hammouch, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Combining classifiers using Dempster-Shafer evidence theory to improve remote sensing images classification</atitle><btitle>2011 International Conference on Multimedia Computing and Systems</btitle><stitle>ICMCS</stitle><date>2011-04</date><risdate>2011</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><isbn>1612847307</isbn><isbn>9781612847306</isbn><eisbn>9781612847313</eisbn><eisbn>1612847323</eisbn><eisbn>9781612847320</eisbn><eisbn>1612847315</eisbn><abstract>Classification system and textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. In this work, we propose to fuse the information outputed by 3 well-known classifiers: Support Vector Machines (SVM), Neural Network (NN) and Parzen Window (PW). These classifiers were combined according to the Dempster-Shafer theory. The input textural feature used are selected according the GMMFS algorithm. The classification results show that the proposed method gives high performances than those of classifiers separately considered.</abstract><pub>IEEE</pub><doi>10.1109/ICMCS.2011.5945724</doi><tpages>4</tpages></addata></record> |
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| identifier | ISBN: 1612847307 |
| ispartof | 2011 International Conference on Multimedia Computing and Systems, 2011, p.1-4 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Classifier Combination Dempster-Shafer Theory GMMFS algorithm Image color analysis Pattern recognition Pixel Remote sensing remote sensing images Support vector machines Textural Feature Training |
| title | Combining classifiers using Dempster-Shafer evidence theory to improve remote sensing images classification |
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