Efficient Body of Revolution Method of Moments for rotationally symmetric antenna systems with offset illumination
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: 2014شناسه الکترونیک: 10.1109/IGARSS.2014.6947409
کلیدواژه(گان): geophysical image processing,image classification,vegetation mapping,China,Southeast Beijing City,adaptive threshold,agricultural landscapes,hard classification,soft classification,vegetation distributions,vegetation mapping,Accuracy,Adaptation models,Biological system modeling,Materials,Remote sensing,Support vector machines,Vegetation mapping,Hard classification,adaptive threshold,linear spectral mixture models,soft classification,support vector machines
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Efficient Body of Revolution Method of Moments for rotationally symmetric antenna systems with offset illumination
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| contributor author | Meincke, P. , Jorgensen, E. | |
| date accessioned | 2020-03-12T20:46:03Z | |
| date available | 2020-03-12T20:46:03Z | |
| date issued | 2014 | |
| identifier other | 6905059.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1024717?locale-attribute=fa | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Efficient Body of Revolution Method of Moments for rotationally symmetric antenna systems with offset illumination | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8149687 | |
| subject keywords | geophysical image processing | |
| subject keywords | image classification | |
| subject keywords | vegetation mapping | |
| subject keywords | China | |
| subject keywords | Southeast Beijing City | |
| subject keywords | adaptive threshold | |
| subject keywords | agricultural landscapes | |
| subject keywords | hard classification | |
| subject keywords | soft classification | |
| subject keywords | vegetation distributions | |
| subject keywords | vegetation mapping | |
| subject keywords | Accuracy | |
| subject keywords | Adaptation models | |
| subject keywords | Biological system modeling | |
| subject keywords | Materials | |
| subject keywords | Remote sensing | |
| subject keywords | Support vector machines | |
| subject keywords | Vegetation mapping | |
| subject keywords | Hard classification | |
| subject keywords | adaptive threshold | |
| subject keywords | linear spectral mixture models | |
| subject keywords | soft classification | |
| subject keywords | support vector machines | |
| identifier doi | 10.1109/IGARSS.2014.6947409 | |
| journal title | ntennas and Propagation Society International Symposium (APSURSI), 2014 IEEE | |
| filesize | 214371 | |
| citations | 0 |