采用Ⅰ-PSPNet语义分割模型的高分辨率遥感影像某特种植物种植地块提取研究  

Research on Extracting Special Plant Planting Plots from High-resolution Remote Sensing Images Using I-PSPNet Semantic Segmentation Model

作　　者：卢志刚 陈芳淼 袁超 田亦陈 陈强 文美平 尹锴 杨光 LU Zhigang;CHEN Fangmiao;YUAN Chao;TIAN Yichen;CHEN Qiang;WEN Meiping;YIN Kai;YANG Guang(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院空天信息创新研究院,北京100101 [2]中国科学院大学,北京100049

出　　处：《遥感技术与应用》2024年第1期222-233,共12页Remote Sensing Technology and Application

基　　金：高分辨率对地观测系统重大专项“高分遥感在电子政务地理空间基础信息库建设与服务中的应用示范(二期)”(00-Y30B01-9001-22/23)。

摘　　要：快速准确获取某特种植物种植地块的面积信息对于毒品产量估算和防范毒品犯罪活动等具有重要意义。针对高分辨率遥感影像中现有某特种植物种植地块检测算法无法同时快速获取位置信息与面积信息的问题,提出了一种适用于快速准确提取某特种植物种植地块的改进PSPNet语义分割模型。通过引入通道注意力SE模块解决了某特种植物种植地块分割存在孔洞的问题,加入Dice Loss损失函数改善了正负样本不平衡的问题,引入编码器—解码器结构使提取的某特种植物种植地块轮廓边界更精确。通过使用MobileNetv2骨干网络将模型预测速度提高了90%。改进得到的Ⅰ-PSPNet模型在某特种植物种植地块提取中MPA和MⅠoU达到95%和84%,检测效率达到84 fps。通过Ⅰ-PSPNet与UNet、Deeplabv3+、PSPNet 3个模型的对比实验表明,改进模型的预测精度和速度均优于上述3个模型,其中,MPA提高了24%、7.4%和7.7%,MⅠoU提高了19%、4.3%和4.9%,预测速度提高了57 fps、56 fps和40 fps。同时,改进后的模型对于RGB波段数据集和GF-2影像也有着良好的适用能力。研究提出的改进模型可用于快速精准获取某特种植物种植地块位置信息和面积信息,可以为禁毒部门快速发现非法种植某特种植物种植地块,客观评估非法种植规模,实施精准打击治理毒品非法犯罪活动提供技术支撑。Quickly and accurately obtaining information on the area of special plant planting plots is of great sig-nificance for drug production estimation and prevention of drug criminal activities.Aiming at the problem that existing special plant planting plot detection algorithms in high-resolution remote sensing images cannot quickly obtain location information and area information at the same time,this paper proposes an improved PSPNet se-mantic segmentation model suitable for quickly and accurately extracting certain special plant planting plots..By introducing the channel attention SE module,the problem of holes in the segmentation of a certain special plant planting plot is solved.The Dice Loss loss function is added to improve the problem of imbalance of positive and negative samples.The encoder-decoder structure is introduced to make the extracted special plant planting Lot outline boundaries are more precise.By using the MobileNetv2 backbone network,the model prediction speed is increased by 90%.The improved I-PSPNet model achieved 95%and 84%MPA and 84%MIoU in the ex-traction of a special plant planting plot,and the detection efficiency reached 84 fps.Comparative experiments be-tween I-PSPNet and UNet,Deeplabv3+,and PSPNet show that the prediction accuracy and speed of the im-proved model are better than the above three models.Among them,MPA increased by 24%,7.4%,and 7.7%,and MIoU increased by 24%,7.4%,and 7.7%.19%,4.3%and 4.9%,predicted speed improvements of 57 fps,56 fps and 40 fps.At the same time,the improved model also has good applicability to RGB band da-ta sets and GF-2 images.The improved model proposed in this article can be used to quickly and accurately ob-tain the location information and area information of a special plant planting plot,and help the anti-drug depart-ment quickly discover the illegal planting of a special plant planting plot,objectively assess the scale of illegal planting,and implement precise crackdowns on illegal drug and criminal activities.Provide technical support.

关 键 词：深度学习 语义分割 Ⅰ-PSPNet 某特种植物种植地块提取 高分辨率遥感影像 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP751[自动化与计算机技术—控制科学与工程] Q949.9[生物学—植物学]