Fault tolerance of SVM algorithm for hyperspectral image
| Autor: | Zhengwu Yuan, Hao Zhang, Yabo Cui, Yuanfeng Wu, Lianru Gao |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Contextual image classification
business.industry Computer science Hyperspectral imaging Pattern recognition Fault tolerance computer.software_genre Support vector machine ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence Data pre-processing Data mining business Classifier (UML) computer Algorithm |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2196704 |
| Popis: | One of the most important tasks in analyzing hyperspectral image data is the classification process[1]. In general, in order to enhance the classification accuracy, a data preprocessing step is usually adopted to remove the noise in the data before classification. But for the time-sensitive applications, we hope that even the data contains noise the classifier can still appear to execute correctly from the user’s perspective, such as risk prevention and response. As the most popular classifier, Support Vector Machine (SVM) has been widely used for hyperspectral image classification and proved to be a very promising technique in supervised classification[2]. In this paper, two experiments are performed to demonstrate that for the hyperspectral data with noise, if the noise of the data is within a certain range, SVM algorithm is still able to execute correctly from the user’s perspective. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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