| 000 | 02784cam a2200349 a 4500 | ||
|---|---|---|---|
| 003 | EG-GiCUC | ||
| 005 | 20250223031525.0 | ||
| 008 | 160525s2016 ua h f m 000 0 eng d | ||
| 040 |
_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aPh.D | ||
| 099 | _aCai01.20.04.Ph.D.2016.Mo.D | ||
| 100 | 0 | _aMohamed Abdou Saeed Almoghalis | |
| 245 | 1 | 2 |
_aA Data mining algorithm for hyperspectral images / _cMohamed Abdou Saeed Almoghalis ; Supervised Osman Hegazy Mohamed , Ibrahim Fahmi Imam , Ali Hamed Elbastawessi |
| 246 | 1 | 5 | _aخوارزمية للتنقيب فى بيانات الصور الفائقة الطيفية |
| 260 |
_aCairo : _bMohamed Abdou Saeed Almoghalis , _c2016 |
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| 300 |
_a80 Leaves : _bfacsimiles ; _c30cm |
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| 502 | _aThesis (Ph.D.) - Cairo University - Faculty of Computers and Information - Department of Information Systems | ||
| 520 | _aHyperspectral images (HSI) are used as a source of information to detect and discriminate the differences between land scales on Earth. In addition, HSI is used to study the changes that occur on these areas. Many unsupervised learning algorithms have been proposed to analyze and interpret the contents of Hyperspectral Images. However, these algorithms are tested against images with ground truth data or benchmark. For clustering algorithms, the number of clusters were identified according to prior knowledge known of such terrains. In practical, such knowledge are unknown. Identifying the correct number of clusters may differ from one algorithm to another. This study introduces a novel methodology to propose the correct number of clusters for clustering algorithms. This is done using by training a Support Vector Machine (SVM) classifier on the spectral information of a benchmark data. The spectral information is binary labeled either by correctly or incorrectly clustered. The purpose of the SVM classifier is to learn where the clustering algorithm can correctly cluster a pixel in the spectral space. Next, the clustering is applied on HSI with no ground truth. SVM classifier is used to validate the performance of the applied unsupervised learning algorithm applied to indicate the appropriate number of clusters. External cluster evaluation measures, Normalized Mutual Information of Purity Index, are used to accomplish this task | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aAlgorithm | |
| 653 | 4 | _aData mining | |
| 653 | 4 | _aHyperspectral images | |
| 700 | 0 |
_aAli Hamed Elbastawessi , _eSupervisor |
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| 700 | 0 |
_aIbrahim Fahmi Imam , _eSupervisor |
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| 700 | 0 |
_aOsman Hegazy Mohamed , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
| 905 |
_aNazla _eRevisor |
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| 905 |
_aSoheir _eCataloger |
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| 942 |
_2ddc _cTH |
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| 999 |
_c57013 _d57013 |
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