基于语义分割网络的冬小麦遥感分类及变化分析  

Classification and change analysis of winter wheat using remote sensing based on semantic segmentation network

作　　者：孙常建 尚永福 王石岩 窦小楠 SUN Changjian;SHANG Yongfu;WANG Shiyan;DOU Xiaonan(Henan Institute of Geographic Information,Zhengzhou 450000,China)

机构地区：[1]河南省地理信息院,河南郑州450000

出　　处：《测绘通报》2024年第10期151-156,共6页Bulletin of Surveying and Mapping

基　　金：2020年度河南省自然资源科研项目(2020-8)。

摘　　要：针对传统机器学习作物遥感分类模型泛化能力较弱等问题,本文评价并验证了不同语义分割网络在光谱特征和光谱+植被指数特征下的济源市冬小麦遥感分类模型的性能和分类精度。结果表明,较单一光谱特征,基于U-Net++和DeepLab V3+的光谱+植被指数特征模型损失函数和IoU值分别降低和提高了13.30%和7.83%、7.80%和5.54%。此外,基于U-Net++的2020—2023年冬小麦分类总体精度达93.47%~95.60%,较DeepLab V3+和随机森林分类的总体精度分别提高了0.12%~2.29%和4.84%~7.40%;景观度值也表明基于U-Net++的冬小麦分类结果具有更优的图斑完整度和紧凑度。最后,本文定量评价了不同年份冬小麦种植面积空间变化结果,为复杂地形条件下作物面积监测应用提供了方法支持。To improve the weak generalization ability of traditional machine learning methods in remote sensing crop classification,winter wheat classification models that employs Sentinel-2 images with different feature selections and semantic segmentation networks are tested and evaluated in Jiyuan city,Henan province.The results show that compared to the spectral features,the model loss and IoU values of the DeepLab V3+and U-Net++ based on spectral and vegetation indices are reduced and improved by 13.30% and 7.83%,7.80% and 5.54%,respectively.In addition,the overall accuracy of winter wheat classification results based on U-Net++from 2020 to 2023 is 93.47%~95.60%,which is 0.12%~2.29% and 4.84%~7.40% higher than that of DeepLab V3+ and random forest,respectively.Moreover,the landscape metrics values also indicate that the winter wheat classification results based on U-Net++network perform better patch integrity and compactness.Finally,the change data and spatial distribution of winter wheat based on U-Net++ from 2020 to 2023 are analyzed.It can provide methodological support for practical applications such as crop area monitoring under complex terrain conditions.

关 键 词：丘陵地区 冬小麦 Sentinel-2 语义分割网络 随机森林 

分 类 号：P237[天文地球—摄影测量与遥感]