Histogram matching-based semantic segmentation model for crop classification with Sentinel-2 satellite imagery

ABSTRACT

Accurate and near-real-time crop mapping from satellite imagery is crucial for agricultural monitoring. However, the seasonal nature of crops makes it challenging to rely on traditional machine learning methods and previous samples generated within specific domains. In this study, we improved the histogram matching method for color correction of multi-temporal images and tested the performance and prediction classification accuracy of three semantic segmentation models based on weak samples. Classification experiments were conducted for nine categories in two cities in Henan province from 2019 to 2022 using 10 m resolution Sentinel-2 images with different feature selection schemes. We trained the models using classified and recorrected results in four selected sites in 2019 and 2020, and designed experiments to assess the performance of the improved histogram matching method and verify the transferability of semantic segmentation models across regions and years. The experimental results showed that the UNet++ model with feature selection and improved histogram matching methods outperformed other models, such as DeepLab V3+ and UNet, in crop classification transfer cases, with better model performance and higher classification accuracy. The UNet++ model without training samples achieved optimal overall accuracy, Kappa coefficient, and mean F1-score values from 2019 to 2022, exceeding 87%, 82%, and 65%, respectively. Moreover, the representative error of weak samples and prediction classification results were analyzed to improve the model robustness. As an application of transfer-learning in crop mapping, the proposed model effectively addressed the classification problem of multispectral satellite imagery with missing labels.

KEYWORDS:

• Semantic segmentation
• histogram matching
• crop classification
• Sentinel-2
• multi-temporal imagery

Acknowledgments

We sincerely thank the anonymous reviewers for their constructive comments and insightful suggestions, which greatly improved the quality of this manuscript. We also acknowledge the support from the Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Lijun Wang: Conceptualization, methodology, writing, reviewing, and editing. Yang Bai: Investigation, visualization, and editing. Jiayao Wang: Conceptualization, supervision, project administration, and funding acquisition. Zheng Zhou: Editing. Fen Qin: Conceptualization and supervision. Jiyuan Hu: Investigation and editing.

Data availability statement

The code is shared at https://github.com/AgriRS/Crop_DL_netwoks.git.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [grant number U21A2014], National Science and Technology Platform Construction [grant number 2005DKA32300], High Resolution Satellite Project of the State Administration of Science [grant number 80Y50G19-9001-22/23], Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions (Henan University), Ministry of Education [grant number GTYR202203], and the Science and Technology Development Program of Henan Province [Grant number 232102321032]. They also acknowledge the support from the Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem.