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Article

Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data

1
School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
2
Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China
3
Department of Geography and Environment, University of Western Ontario, London, ON N6A 5C2, Canada
4
Institute for Computer Research (IUII), University of Alicante, E-03080 Alicante, Spain
5
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
6
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Academic Editor: Filomena Romano
Remote Sens. 2022, 14(11), 2668; https://doi.org/10.3390/rs14112668
Received: 13 May 2022 / Revised: 30 May 2022 / Accepted: 30 May 2022 / Published: 2 June 2022
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%. View Full-Text
Keywords: polarimetric synthetic aperture radar (PolSAR); crop classification; agriculture; model-based decomposition; RADARSAT-2 polarimetric synthetic aperture radar (PolSAR); crop classification; agriculture; model-based decomposition; RADARSAT-2
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MDPI and ACS Style

Xie, Q.; Dou, Q.; Peng, X.; Wang, J.; Lopez-Sanchez, J.M.; Shang, J.; Fu, H.; Zhu, J. Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data. Remote Sens. 2022, 14, 2668. https://doi.org/10.3390/rs14112668

AMA Style

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

Chicago/Turabian Style

Xie, Qinghua, Qi Dou, Xing Peng, Jinfei Wang, Juan M. Lopez-Sanchez, Jiali Shang, Haiqiang Fu, and Jianjun Zhu. 2022. "Crop Classification Based on the Physically Constrained General Model-Based Decomposition Using Multi-Temporal RADARSAT-2 Data" Remote Sensing 14, no. 11: 2668. https://doi.org/10.3390/rs14112668

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