温宿县台兰河灌区作物的EAA-UNet遥感分类研究

doi:10.14088/j.cnki.issn0439-8114.2025.09.033

摘要/Abstract

摘要： 为准确识别新疆阿克苏地区温宿县台兰河灌区作物种植类型,基于深度学习技术提出了一种改进的遥感影像像元分类方法。采用高分系列卫星数据（包括GF1、GF1B、GF6和GF2）,以GF2融合影像为基准进行精细标注,并通过实地勘察对标注结果进行验证,随后将验证后的标注统一配准至其他影像数据,最终构建了样本尺寸为128 px×128 px的多类别分类数据集。EAA-UNet模型通过集成ECA模块、ASSP多尺度特征提取模块和门控注意力机制模块,显著提升了特征提取性能。采用准确率（Accuracy,A）、精确度（Precision,P）和平均交并比（MIoU）3项指标进行定量评估,并与当前主流的像元分类模型进行对比分析,以验证EAA-UNet模型的优越性。结果表明,EAA-UNet模型的准确率、精确率、平均交并比分别为71.56%、82.79%、69.94%,均高于FCN8s模型、UNet模型、ResUNet模型和DeepLabV3+模型。EAA-UNet模型在台兰河灌区的作物分类任务中表现出色,其预测结果与真实地块种植类型高度吻合。EAA-UNet模型通过引入的改进机制有效解决了该灌区作物分类模糊的问题,不仅实现了灌区种植作物类型的精准识别,更为后续台兰河灌区需水量预测提供了可靠的数据支撑。

关键词: EAA-UNet模型, 遥感影像, 作物, 遥感分类, 温宿县台兰河灌区

Abstract: To accurately identify crop planting types in the Tailan River Irrigation District of Wensu County, Xinjiang, an improved pixel classification method for remote sensing imagery was proposed based on deep learning technology.Using Gaofen series satellite data (including GF1, GF1B, GF6, and GF2), fine annotation was performed based on GF2 fused imagery, and the annotation results were validated through field surveys. The validated annotations were then uniformly registered to other imagery data, ultimately constructing a multi-category classification dataset with a sample size of 128 px × 128 px.The EAA-UNet model significantly improved feature extraction performance by integrating the ECA module, ASSP multi-scale feature extraction module, and gated attention mechanism module.Quantitative evaluation was conducted using three metrics: Accuracy (A), Precision (P), and Mean Intersection over Union (MIoU), and a comparative analysis with current mainstream pixel classification models was performed to validate the superiority of the EAA-UNet model. The results showed that the EAA-UNet model achieved Accuracy, Precision, and MIoU values of 71.56%, 82.79%, and 69.94%, respectively, all higher than those of the FCN8s, UNet, ResUNet, and DeepLabV3+ models.The EAA-UNet model performed excellently in the crop classification task in the Tailan River Irrigation District, with its predictions highly consistent with the actual planting types of the plots. The EAA-UNet model effectively addressed the ambiguity in crop classification in the irrigation district through its introduced improvements, not only achieving precise identification of planted crop types but also providing reliable data support for subsequent water demand prediction in the Tailan River Irrigation District.

Key words: EAA-UNet model, remote sensing imagery, crop, remote sensing classification, Tailan River Irrigation District of Wensu County

中图分类号:

TP391

李杰, 董峦. 温宿县台兰河灌区作物的EAA-UNet遥感分类研究[J]. 湖北农业科学, 2025, 64(9): 213-219.

LI Jie, DONG Luan. Research on crop remote sensing classification in the Tailan River Irrigation District of Wensu County using the EAA-UNet[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(9): 213-219.

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