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Article

Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network

1
Department of Geography, The University of Western Ontario, London, ON N6A 5C2, Canada
2
School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
3
Applied Geosolutions, Durham, NH 03824, USA
4
Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 832; https://doi.org/10.3390/rs12050832
Received: 6 February 2020 / Revised: 1 March 2020 / Accepted: 2 March 2020 / Published: 4 March 2020
(This article belongs to the Special Issue Deep Learning and Remote Sensing for Agriculture)
Annual crop inventory information is important for many agriculture applications and government statistics. The synergistic use of multi-temporal polarimetric synthetic aperture radar (SAR) and available multispectral remote sensing data can reduce the temporal gaps and provide the spectral and polarimetric information of the crops, which is effective for crop classification in areas with frequent cloud interference. The main objectives of this study are to develop a deep learning model to map agricultural areas using multi-temporal full polarimetric SAR and multi-spectral remote sensing data, and to evaluate the influence of different input features on the performance of deep learning methods in crop classification. In this study, a one-dimensional convolutional neural network (Conv1D) was proposed and tested on multi-temporal RADARSAT-2 and VENµS data for crop classification. Compared with the Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN) and non-deep learning methods including XGBoost, Random Forest (RF), and Support Vector Machina (SVM), the Conv1D performed the best when the multi-temporal RADARSAT-2 data (Pauli decomposition or coherency matrix) and VENµS multispectral data were fused by the Minimum Noise Fraction (MNF) transformation. The Pauli decomposition and coherency matrix gave similar overall accuracy (OA) for Conv1D when fused with the VENµS data by the MNF transformation (OA = 96.65 ± 1.03% and 96.72 ± 0.77%). The MNF transformation improved the OA and F-score for most classes when Conv1D was used. The results reveal that the coherency matrix has a great potential in crop classification and the MNF transformation of multi-temporal RADARSAT-2 and VENµS data can enhance the performance of Conv1D. View Full-Text
Keywords: crop classification; RADARSAT-2; VENµS; data fusion; deep learning; convolutional neural network crop classification; RADARSAT-2; VENµS; data fusion; deep learning; convolutional neural network
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MDPI and ACS Style

Liao, C.; Wang, J.; Xie, Q.; Baz, A.A.; Huang, X.; Shang, J.; He, Y. Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network. Remote Sens. 2020, 12, 832. https://doi.org/10.3390/rs12050832

AMA Style

Liao C, Wang J, Xie Q, Baz AA, Huang X, Shang J, He Y. Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network. Remote Sensing. 2020; 12(5):832. https://doi.org/10.3390/rs12050832

Chicago/Turabian Style

Liao, Chunhua; Wang, Jinfei; Xie, Qinghua; Baz, Ayman A.; Huang, Xiaodong; Shang, Jiali; He, Yongjun. 2020. "Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network" Remote Sens. 12, no. 5: 832. https://doi.org/10.3390/rs12050832

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