HYPERSPECTRAL SENSOR DATA FUSION AT DECISION LEVEL USING SUPPORT VECTOR MACHINE
| Autor: | K. Nikitha, V. Sowmya Devi, A. Kiranmai, Y. Sai Praveen, M. Tech |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
Decision level 010504 meteorology & atmospheric sciences business.industry Feature extraction 0211 other engineering and technologies Mode (statistics) Hyperspectral imaging Pattern recognition 02 engineering and technology Sensor fusion computer.software_genre 01 natural sciences Panchromatic film Support vector machine ComputingMethodologies_PATTERNRECOGNITION Data mining Artificial intelligence High dimensionality business computer 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | International Journal of Research in Engineering and Technology. :14-18 |
| ISSN: | 2319-1163 2321-7308 |
| DOI: | 10.15623/ijret.2016.0524005 |
| Popis: | SVM is the major feature extraction technique extensively used in the hyperspectral sensor. First, analysis aimed at understanding and assessing the potentialities of SVM classifiers in data fusion at decision level is presented. This is very significant particularly for improving the algorithms for classification of remote sensing data from different types of sensors operating in hyperspectral or panchromatic mode. The major problem occurs with high dimensionality of hyperspectral sensor data and nonlinear characterization, SVM can overcome these problems and deliver a better result when compared with other classifiers at decision level. The performance and efficiency of SVM depend upon the problem and different kernel functions used in SVM classifier at decision level data fusion. In this paper, problems in the classification of hyperspectral sensor data are analysed and applicability of decision level data fusion as a part of the enhancement of sensor data capability is studied. |
| Databáze: | OpenAIRE |
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