用无人机P波段雷达+深度学习，精准识别地下蚁巢
Ant Nest Detection Using Underground P-Band TomoSAR
- 无人机搭载P波段SAR，螺旋飞行获取森林地下三维图像
- 结合卷积神经网络，实现100%检测准确率，误报为0
- 适合林业管理、病虫害监测等场景，可大规模应用
叶切蚁在巴西商业林中造成严重落叶，导致生物质和产量损失。它们构建复杂的地下巢穴，亟需高效监测手段获取大面积地下信息。本研究通过电磁仿真模拟6至100个地下腔室的蚁巢，分析其雷达特征。随后开展实地实验，使用无人机载P波段合成孔径雷达（SAR），采用螺旋飞行模式生成商业化桉树林的高分辨率地面层析图。利用卷积神经网络（CNN）从层析数据中检测蚁巢并估算大小，结果表明：蚁巢检测准确率达100%，误报率为0%，尺寸估计平均误差为21%。该方法展示了将合成孔径雷达（SAR）与机器学习结合，在商业林业监测与管理中的巨大潜力。
原文摘要 · Abstract (English)
Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture Radar (SAR) systems provide a powerful solution for this challenge. This study presents the results of electromagnetic simulations designed to detect leaf-cutting ant nests in industrial forests. The simulations modeled nests with 6 to 100 underground chambers, offering insights into their radar signatures. Following these simulations, a field study was conducted using a drone-borne SAR operating in the P-band. A helical flight pattern was employed to generate high-resolution ground tomography of a commercial eucalyptus forest. A convolutional neural network (CNN) was implemented to detect ant nests and estimate their sizes from tomographic data, delivering remarkable results. The method achieved an ant nest detection accuracy of 100%, a false alarm rate of 0%, and an average error of 21% in size estimation. These outcomes highlight the transformative potential of integrating Synthetic Aperture Radar (SAR) systems with machine learning to enhance monitoring and management practices in commercial forestry.
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