A soft computing framework for image classification based on recurrence plots
Suitable time series representations play an important role in classification tasks. In this letter, we investigate the use of recurrence-plot-(RP)-based representations in the classification of eucalyptus regions in remote sensing images. The proposed framework is composed of three steps. First, time series associated with image pixels are represented by RP images; next, RP images are characterized by means of visual description approaches; finally, we use a soft computing framework based on genetic programing to discover an effective combination of time series dissimilarity functions to combine extracted features. Performed experiments in a eucalyptus classification problem demonstrated that the proposed framework is effective when compared to approaches based on the use of time series itself.
| Main Authors: | , , , , , , , |
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| Format: | article biblioteca |
| Language: | eng |
| Subjects: | F30 - Génétique et amélioration des plantes, K10 - Production forestière, télédétection, Eucalyptus, classification, logiciel, imagerie par satellite, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_2683, http://aims.fao.org/aos/agrovoc/c_1653, http://aims.fao.org/aos/agrovoc/c_24008, http://aims.fao.org/aos/agrovoc/c_36761, |
| Online Access: | http://agritrop.cirad.fr/589936/ http://agritrop.cirad.fr/589936/1/2018Menini_SoftComputingFramework_GRSL.pdf |
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| Summary: | Suitable time series representations play an important role in classification tasks. In this letter, we investigate the use of recurrence-plot-(RP)-based representations in the classification of eucalyptus regions in remote sensing images. The proposed framework is composed of three steps. First, time series associated with image pixels are represented by RP images; next, RP images are characterized by means of visual description approaches; finally, we use a soft computing framework based on genetic programing to discover an effective combination of time series dissimilarity functions to combine extracted features. Performed experiments in a eucalyptus classification problem demonstrated that the proposed framework is effective when compared to approaches based on the use of time series itself. |
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