Abstract
Due to the unique advantages of microwave detection, such as its low restriction from the atmosphere and its capability to obtain structural information about ground targets, synthetic aperture radar (SAR) is increasingly used in agricultural observations. However, while SAR data has shown great potential for large-scale crop mapping, there have been few studies on the use of SAR images for large-scale multispecies crop classification at present. In this paper, a large-scale crop mapping method using multi-temporal dual-polarization SAR data was proposed. To reduce multi-temporal SAR data redundancy, a multi-temporal images optimization method based on analysis of variance (ANOVA) and Jeffries–Matusita (J–M) distance was applied to the time series of images after preprocessing to select the optimal images. Facing the challenges from smallholder farming modes, which caused the complex crop planting patterns in the study area, U-Net, an improved fully convolutional network (FCN), was used to predict the different crop types. In addition, the batch normalization (BN) algorithm was introduced to the U-Net model to solve the problem of a large number of crops and unbalanced sample numbers, which had greatly improved the efficiency of network training. Finally, we conducted experiments using multi-temporal Sentinel-1 data from Fuyu City, Jilin Province, China in 2017, and we obtained crop mapping results with an overall accuracy of 85% as well as a Kappa coefficient of 0.82. Compared with the traditional machine learning methods (e.g., random forest (RF) and support vector machine (SVM)), the proposed method can still achieve better classification performance under the condition of a complex crop planting structure.
Highlights
China supplies 21% of the world’s population with only 7% of the world’s arable land
(1) (1)Considering the number of various types of crop in the training sample set is uneven, which is caused by the disproportion acreage of different crops in actual production, this paper caused by the disproportion acreage of different crops in actual production, this paper introduces introduces the BN algorithm [43] into the U-Net network model, that is, adding a batch the BN algorithm [42] into the U-Net network model, that is, adding a batch normalization normalization layer between convolution layer and ReLU in each neural unit of U-Net, to layer between convolution layer and ReLU in each neural unit of U-Net, to improve the network improve the network training efficiency
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Summary
China supplies 21% of the world’s population with only 7% of the world’s arable land. With the development of agricultural modernization in recent years, problems in agricultural development in China, such as the agricultural foundation, are weak; the quality and safety of agricultural products are more problematic, and the structural imbalance of agricultural production as well as its agricultural benefits are relatively low, even if having become more prominent. These problems restrict the development of China’s agriculture [1]. The training environment and parameters of the U-Net is
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