Abstract

A deep semantic segmentation model-based method can achieve state-of-the-art accuracy and high computational efficiency in large-scale crop mapping. However, the model cannot be widely used in actual large-scale crop mapping applications, mainly because the annotation of ground truth data for deep semantic segmentation model training is time-consuming. At the operational level, it is extremely difficult to obtain a large amount of ground reference data by photointerpretation for the model training. Consequently, in order to solve this problem, this study introduces a workflow that aims to extract rice distribution information in training sample shortage regions, using a deep semantic segmentation model (i.e., U-Net) trained on pseudo-labels. Based on the time series Sentinel-1 images, Cropland Data Layer (CDL) and U-Net model, the optimal multi-temporal datasets for rice mapping were summarized, using the global search method. Then, based on the optimal multi-temporal datasets, the proposed workflow (a combination of K-Means and random forest) was directly used to extract the rice-distribution information of Jiangsu (i.e., the K–RF pseudo-labels). For comparison, the optimal well-trained U-Net model acquired from Arkansas (i.e., the transfer model) was also transferred to Jiangsu to extract local rice-distribution information (i.e., the TF pseudo-labels). Finally, the pseudo-labels with high confidences generated from the two methods were further used to retrain the U-Net models, which were suitable for rice mapping in Jiangsu. For different rice planting pattern regions of Jiangsu, the final results showed that, compared with the U-Net model trained on the TF pseudo-labels, the rice area extraction errors of pseudo-labels could be further reduced by using the U-Net model trained on the K–RF pseudo-labels. In addition, compared with the existing rule-based rice mapping methods, he U-Net model trained on the K–RF pseudo-labels could robustly extract the spatial distribution information of rice. Generally, this study could provide new options for applying a deep semantic segmentation model to training sample shortage regions.

Highlights

  • Comparison of Rice Mapping Performance with Other Rice Mapping Methods (RQ3) Table 4 and Figure 11 report the comparison between the U-Net trained on the K–random forest methods (RF) pseudo-labels and the two-rule-based rice mapping methods

  • For the three parts of the target region with different rice planting patterns, the root mean square error (RMSE) (RRMSE) of the U-Net trained on the K–RF pseudo-labels was always the smallest, compared with that of the other two methods

  • At the rice area extraction level, compared with the two-rule-based methods, the U-Net trained on the K–RF pseudo-labels could robustly extract rice spatial distribution information in the regions with different rice planting patterns

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Summary

Introduction

For the remote sensing community, traditional machine learning algorithms have been well adapted to the application of crop classification in recent years [1,2,3,4], especially for supervised classifiers trained on pixel point samples. Different from independent pixel point sample sets used by traditional supervised classifiers, training sample sets of the DCNN model are composed of a large number of annotated image sets [22,23,24] This means that all pixel points in a continuous space need to be annotated to construct samples required for DCNN model training; the process is very time-consuming, at an operational level, and it is unfeasible to construct a large number of ground reference image sets through visual interpretation [25], limiting its application in large-scale crop mapping

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