Fuzzy-fusion approach for land cover classification
| Autor: | Rita A. Ribeiro, Tiago M. A. Santos, André Mora, João M. N. Silva |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Image fusion
Artificial neural network business.industry Computer science Multispectral image Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Decision tree Inference Pattern recognition 02 engineering and technology Land cover Machine learning computer.software_genre ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) computer 021101 geological & geomatics engineering |
| Zdroj: | 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES). |
| DOI: | 10.1109/ines.2016.7555116 |
| Popis: | The use of computational intelligent techniques for feature extraction and classification from earth observation satellite images, like Landsat multispectral images, can contribute to improve remote sensing analysis. Image fusion techniques are applied to fuse the spectral images into a higher-level image of the land cover distribution. In this paper we propose a fuzzy-fusion inference approach for satellite image classification based on a fuzzy process, which uses both a hybrid method to train the classifier and reinforcement aggregation operators in the inference scheme. The approach was tested with land cover maps for the district of Mandimba of the Niassa province, Mozambique and was validated against an expert classification and then with Decision trees and Artificial Neural Networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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