An approach for hyperspectral image classification by optimizing SVM using self organizing map
| Autor: | Amit Sinhal, Haoxiang Wang, Deepak Kumar Jain, Rishin Kumar Choubey, Surendra Bilouhan Dubey, Amar Jain, Siddharth Kumar Arjaria |
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| Rok vydání: | 2018 |
| Předmět: |
Self-organizing map
General Computer Science Pixel Computer science Posterior probability 0211 other engineering and technologies Confusion matrix 02 engineering and technology computer.software_genre Theoretical Computer Science Image (mathematics) Support vector machine Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Hyperspectral image classification 020201 artificial intelligence & image processing Data mining computer 021101 geological & geomatics engineering |
| Zdroj: | Journal of Computational Science. 25:252-259 |
| ISSN: | 1877-7503 |
| DOI: | 10.1016/j.jocs.2017.07.016 |
| Popis: | In this paper, an efficient technique for the classification of Hyper-Spectral Images taken form satellite is actualized. The Proposed Methodology is based on the concept of optimizing Support Vector Machine (SVM) using Self Organizing Maps (SOM) and then classification of Interior and Exterior Pixels can be done by the comparing the Posterior Probability of each of the pixel intensities. The Methodology applied here works in two phases, first is to train the important features from the image by Optimizing Support Vector Machine using Self Organizing Maps and second is to find the Interior and Exterior Pixels and Comparing Optimal Threshold and Probability. The Experimental results are performed on two datasets which consists of 16 and 9 classes such as corn-no till, corn, soybeans-no till, corn-min till, soybeans-clean till, soybeans min till alfalfa, grass/trees, grass/pasture, grass/pasture-mowed, oats, hay windrowed wheat, woods, stone-steel towers and building-grass-trees-drives. The proposed Methodology Outperforms better in comparison with the existing Classification methodology in terms of Accuracy and Kappa and Confusion Matrix. |
| Databáze: | OpenAIRE |
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