数字农科院2.0

A cost-effective and robust mapping method for diverse crop types using weakly supervised semantic segmentation with sparse point samples

文献类型： 外文期刊

作者： Zhiwen Cai;Baodong Xu;Qiangyi Yu;Xinyu Zhang;Jingya Yang;Haodong Wei;Shiqi Li;Qian Song;Hang Xiong;Hao Wu;Wenbin Wu;Zhihua Shi;Qiong Hu

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关键词： Crop type mapping;Limited sparse point annotations;multi-temporal Sentinel-2 images;Weakly supervised semantic segmentation

期刊名称： ISPRS Journal of Photogrammetry and Remote Sensing

ISSN： 0924-2716

年卷期： 2024 年 218 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： Accurate and timely information on the spatial distribution and areas of crop types is critical for yield estimation, agricultural management, and sustainable development. However, traditional crop classification methods often struggle to identify various crop types effectively due to their intricate spatiotemporal patterns and high training data demands. To address this challenge, we developed a Structure-aware Label eXpansion segmentation Network (StructLabX-Net) for diverse crop type mapping using limited point-annotated samples. StructLabX-Net features a backbone U-TempoNet, which combines CNNs and LSTM to explore intricate spatiotemporal patterns. It also incorporates multi-task weak supervision heads for edge detection and pseudo-label expansion, adding crucial structure and contextual insights. We tested the StructLabX-Net across three distinct regions in China, assessing over 10 crop types and comparing its performance against five popular classifiers based on multi-temporal Sentinel-2 images. The results showed that StructLabX-Net significantly outperformed RF, SVM, DeepCropMapping, Transformer, and patch-based CNN in identifying various crop types across three regions with sparse training samples. It achieved the highest overall accuracy and mean F1-score: 91.0% and 89.1% in Jianghan Plain, 91.5% and 90.7% in Songnen Plain, as well as 91.0% and 90.8% in Sanjiang Plain. StructLabX-Net demonstrated a particular advantage for those “hard types” characterized by limited samples and complex phenological features. Furthermore, ablation experiments highlight the crucial role of the “edge” head in guiding the model to accurately differentiate between various crop types with clearer class boundaries, and the “expansion” head in refining the understanding of target crops by providing extra details in pseudo-labels. Meanwhile, combining our backbone U-TempoNet with multi-task weak supervision heads exhibited superior results of crop type mapping than those derived by other segmentation models. Overall, StructLabX-Net maximizes the utilization of limited sparse samples from field surveys, offering a simple, cost-effective, and robust solution for accurately mapping various crop types at large scales. The code will be publicly available at https://github.com/BruceKai/StructLabX-Net.

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[1]Evaluation of five deep learning models for crop type mapping using sentinel-2 time series images with missing information. Hongwei Zhao,Sibo Duan,Jia Liu,Liang Sun,Louis Reymondin. 2021

[2]Improving crop type mapping by integrating LSTM with temporal random masking and pixel-set spatial information. Xinyu Zhang,Zhiwen Cai,Qiong Hu,Jingya Yang,Haodong Wei,Liangzhi You,Baodong Xu. 2024

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