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首页> 外文期刊>Journal of Applied Remote Sensing >Two-pathway anti-interference neural network based on the retinal perception mechanism for classification of remote sensing images from unmanned aerial vehicles
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Two-pathway anti-interference neural network based on the retinal perception mechanism for classification of remote sensing images from unmanned aerial vehicles

机译：基于视网膜抗干扰机制的双途径抗干扰神经网络遥感图像从无人驾驶飞行器分类

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摘要

The ultrahigh resolution of unmanned aerial vehicle (UAV) remote sensing images and tilting photography with multiple perspectives provide complete and detailed ground observation data for various engineering applications. However, noise and interference information make learning the typical features of ground objects difficult for current deep learning semantic segmentation networks. The hierarchical cognitive structure of human vision and the information transmission modes of retinal cone and rod cells were used to design a two-pathway anti-interference network for retinal perception mechanism simulation (RPMS). In the first pathway, the hierarchical cognition of cone cells was simulated by a one-to-one connected multiscale dilated convolution structure. In the second pathway, the hierarchical cognition of rod cells was simulated by a multiscale pyramid structure with many-to-one connections. With the one-to-one connection, the ability of RPMS to recognize detailed edges was strengthened. Furthermore, the many-to-one connection helped RPMS resist the disturbance from noise and interference. By combining the feature maps of the two paths, RPMS exhibited stronger noise resistance, better texture recognition, and better detail recognition compared with other semantic segmentation networks in the classification experiments. Thus this technique is suitable for UAV remote sensing image classification and has a broad application potential. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：具有多个观点的无人空中飞行器（UAV）遥感图像和倾斜摄影的超高分辨率为各种工程应用提供了完整和详细的地面观察数据。然而，噪声和干扰信息使学习对当前深度学习语义分割网络难以实现地面对象的典型特征。人类视觉的分层认知结构和视网膜锥和杆电池的信息传输模式用于设计一种用于视网膜感知机制模拟（RPMS）的双途抗干扰网络。在第一途径中，通过一对一连接的多尺寸扩张的卷积结构模拟锥形电池的分层认知。在第二途径中，通过多尺度金字塔结构模拟具有多个连接的多尺度金字塔结构的分层认知。通过一对一的连接，加强了RPM识别详细边缘的能力。此外，多对一的连接有助于RPM抵抗噪声和干扰的干扰。通过组合两条路径的特征图，与分类实验中的其他语义分割网络相比，RPMS表现出更强的抗噪声性，更好的纹理识别和更好的细节识别。因此，该技术适用于UAV遥感图像分类并且具有广泛的应用势。 （c）2020光学仪表工程师协会（SPIE）

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