Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data
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http://hdl.handle.net/10045/124297
Títol: | Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data |
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Autors: | Xie, Qinghua | Dou, Qi | Peng, Xing | Wang, Jinfei | Lopez-Sanchez, Juan M. | Shang, Jiali | Fu, Haiqiang | Zhu, Jianjun |
Grups d'investigació o GITE: | Señales, Sistemas y Telecomunicación |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Paraules clau: | Polarimetric synthetic aperture radar (PolSAR) | Crop classification | Agriculture | Model-based decomposition | RADARSAT-2 |
Àrees de coneixement: | Teoría de la Señal y Comunicaciones |
Data de publicació: | 2-de juny-2022 |
Editor: | MDPI |
Citació bibliogràfica: | Xie Q, Dou Q, Peng X, Wang J, Lopez-Sanchez JM, Shang J, Fu H, Zhu J. Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data. Remote Sensing. 2022; 14(11):2668. https://doi.org/10.3390/rs14112668 |
Resum: | Crop identification and classification are of great significance to agricultural land use management. The physically constrained general model-based decomposition (PCGMD) has proven to be a promising method in comparison with the typical four-component decomposition methods in scattering mechanism interpretation and identifying vegetation types. However, the robustness of PCGMD requires further investigation from the perspective of final applications. This paper aims to validate the efficiency of the PCGMD method on crop classification for the first time. Seven C-band time-series RADARSAT-2 images were exploited, covering the entire growing season over an agricultural region near London, Ontario, Canada. Firstly, the response and temporal evolution of the four scattering components obtained by PCGMD were analyzed. Then, a forward selection approach was applied to achieve the highest classification accuracy by searching an optimum combination of multi-temporal SAR data with the random forest (RF) algorithm. For comparison, the general model-based decomposition method (GMD), the original and its three improved Yamaguchi four-component decomposition approaches (Y4O, Y4R, S4R, G4U), were used in all tests. The results reveal that the PCGMD method is highly sensitive to seasonal crop changes and matches well with the real physical characteristics of the crops. Among all test methods used, the PCGMD method using six images obtained the optimum classification performance, reaching an overall accuracy of 91.83%. |
Patrocinadors: | This research was funded in part by the National Natural Science Foundation of China (Grant No. 41804004, 41820104005, 42171387, 42101400, 41904004), the Canadian Space Agency SOAR-E Program (Grant No. SOAR-E-5489), and the Spanish Ministry of Science and Innovation (Grant No. PID2020-117303GB-C22). |
URI: | http://hdl.handle.net/10045/124297 |
ISSN: | 2072-4292 |
DOI: | 10.3390/rs14112668 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.3390/rs14112668 |
Apareix a la col·lecció: | INV - SST - Artículos de Revistas |
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