首页|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

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

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

Deep learningSemantic segmentationI-PSPNetExtraction of special plant planting plotsHigh-resolution remote sensing images

卢志刚、陈芳淼、袁超、田亦陈、陈强、文美平、尹锴、杨光

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中国科学院空天信息创新研究院,北京 100101

中国科学院大学,北京 100049

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

高分辨率对地观测系统重大专项

00-Y30B01-9001-22/23

2024

遥感技术与应用
中国科学院遥感联合中心

遥感技术与应用

CSTPCD北大核心
影响因子:0.961
ISSN:1004-0323
年,卷(期):2024.39(1)
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