An Optimized Semi-Supervised Generative Adversarial Network Rice Extraction Method Based on Time-Series Sentinel Images

Lingling Du, Zhijun Li (), Qian Wang, Fukang Zhu and Siyuan Tan
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Lingling Du: College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China
Zhijun Li: College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China
Qian Wang: Spatial Information Acquisition and Application Joint Laboratory of Anhui Province, Tongling 244061, China
Fukang Zhu: College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China
Siyuan Tan: College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China

Agriculture, 2024, vol. 14, issue 9, 1-27

Abstract: In response to the limitations of meteorological conditions in global rice growing areas and the high cost of annotating samples, this paper combines the Vertical-Vertical (VV) polarization and Vertical-Horizontal (VH) polarization backscatter features extracted from Sentinel-1 synthetic aperture radar (SAR) images and the NDVI, NDWI, and NDSI spectral index features extracted from Sentinel-2 multispectral images. By leveraging the advantages of an optimized Semi-Supervised Generative Adversarial Network (optimized SSGAN) in combining supervised learning and semi-supervised learning, rice extraction can be achieved with fewer annotated image samples. Within the optimized SSGAN framework, we introduce a focal-adversarial loss function to enhance the learning process for challenging samples; the generator module employs the Deeplabv3+ architecture, utilizing a Wide-ResNet network as its backbone while incorporating dropout layers and dilated convolutions to improve the receptive field and operational efficiency. Experimental results indicate that the optimized SSGAN, particularly when utilizing a 3/4 labeled sample ratio, significantly improves rice extraction accuracy, leading to a 5.39% increase in Mean Intersection over Union (MIoU) and a 2.05% increase in Overall Accuracy (OA) compared to the highest accuracy achieved before optimization. Moreover, the integration of SAR and multispectral data results in an OA of 93.29% and an MIoU of 82.10%, surpassing the performance of single-source data. These findings provide valuable insights for the extraction of rice information in global rice-growing regions.

Keywords: rice extraction; synthetic aperture radar (SAR); spectral features; SSGAN; remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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