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Open AccessArticle

Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops

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Departamento de Electrónica y Ciencias de la Computación, Pontificia Universidad Javeriana Seccional Cali, Cali 760031, Colombia
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Grenoble Images Parole Signals Automatique Laboratory (GIPSA-Lab), Grenoble Institute of Technology, 38031 Grenoble, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(17), 2683; https://doi.org/10.3390/rs12172683
Received: 30 June 2020 / Revised: 5 August 2020 / Accepted: 10 August 2020 / Published: 19 August 2020
(This article belongs to the Special Issue Land Use/Cover Change Detection with Geospatial Technologies)
The complementary nature of different modalities and multiple bands used in remote sensing data is helpful for tasks such as change detection and the prediction of agricultural variables. Nonetheless, correctly processing a multi-modal dataset is not a simple task, owing to the presence of different data resolutions and formats. In the past few years, graph-based methods have proven to be a useful tool in capturing inherent data similarity, in spite of different data formats, and preserving relevant topological and geometric information. In this paper, we propose a graph-based data fusion algorithm for remotely sensed images applied to (i) data-driven semi-unsupervised change detection and (ii) biomass estimation in rice crops. In order to detect the change, we evaluated the performance of four competing algorithms on fourteen datasets. To estimate biomass in rice crops, we compared our proposal in terms of root mean squared error (RMSE) concerning a recent approach based on vegetation indices as features. The results confirm that the proposed graph-based data fusion algorithm outperforms state-of-the-art methods for change detection and biomass estimation in rice crops. View Full-Text
Keywords: biomass estimation; change detection; data fusion; graph based; multi-modal; multi-temporal; multi-spectral; remote sensing biomass estimation; change detection; data fusion; graph based; multi-modal; multi-temporal; multi-spectral; remote sensing
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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3923125
    Description: Dataset and code used to replicate the results in the paper.
MDPI and ACS Style

Jimenez-Sierra, D.A.; Benítez-Restrepo, H.D.; Vargas-Cardona, H.D.; Chanussot, J. Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops. Remote Sens. 2020, 12, 2683.

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