A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization
When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provi...
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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
IEEE
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/bfd7abd088b9463b9a186e91f96fc3e1 |
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| Résumé: | When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on modeling the physical phenomena occurring on the Earth’s surface. In this article, we introduce a flexible and efficient method based on graph Laplacians for information selection at different levels of data fusion. The proposed approach combines data structure and information content to address the limitations of existing graph-Laplacian-based methods in dealing with heterogeneous datasets. Moreover, it adapts the selection to each homogenous area of the considered images according to their underlying properties. Experimental tests carried out on several multivariate remote sensing datasets show the consistency of the proposed approach. |
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