基于卷积神经网络的遥感图像目标识别仿真

Simulation of Remote Sensing Image Target Recognition Based on Convolutional Neural Network

在遥感图像中,目标往往位于复杂的地物背景中,包括不同类型的植被、土地覆盖、建筑物等。上述复杂的地物背景对目标识别造成了困难。为了精准识别遥感图像目标,提出一种卷积神经网络下遥感图像目标识别算法。将暗通道原理和双边滤波算法有效结合,对遥感图像展开增强处理。统计分析遥感图像目标尺度范围,通过训练和测试卷积神经网络,得到最佳目标感兴趣区域尺度。确定目标感兴趣区域最佳尺度后,构建基于卷积神经网络的遥感图像目标识别架构,完成遥感图像目标识别。通过实验分析证明,采用所提算法可以有效提升遥感图像增强效果,具有较好的遥感图像目标识别性能。

In remote sensing images,targets are often located in complex backgrounds,including different types of vegetation,land cover and buildings.These complex backgrounds pose difficulties for target recognition.In order to accurately recognize the target in remote sensing images,this paper proposed an algorithm for recognizing targets in remote sensing images based on convolutional neural network.Firstly,the dark channel principle and bilateral filtering algorithm were effectively combined to enhance the remote sensing image.Then,the scale range of the target in remote sensing images was statistically analyzed.Through training and testing the convolutional neural network,we got the best scale of the target region of interest.After determining the best scale,we constructed a target recognition architecture based on convolutional neural network.Finally,we completed the recognition of the target in remote sensing images.Experimental analysis proves that the proposed algorithm can effectively improve the enhancement effect of remote sensing images and has good performance in recognizing the target in remote sensing images.