卷积神经网络下的高分二号卫星影像道路提取

Road Extraction of GF-2 Satellite Image Based on Convolutional Neural Network

本文针对深度神经网络对高分二号遥感影像道路提取时细节信息丢失较多、道路周围环境考虑不充分等情况,在已有的研究成果上,提出一种基于全卷积神经网络遥感影像道路提取的改进方案.方案创新研究了全卷积神经网络的算法原理,将预调色后的高分二号影像按一定尺寸分幅输出,将输出图像及标签对应输入于以全卷积神经网络为基础的改进网络,通过结合残差单元以及增加网络层数得到识别精度较高的道路提取图像.实验表明,该方法在同一样本中对高分二号卫星影像道路提取的效果有所提升,道路的完整性和准确性有所提高.

More details may be lost and considerations of the surrounding environment of the road are inadequate when extract the road from GF-2 remote sensing satellite which based on the deep neural network.Aiming at these problems and based on the existing researches results,this study proposes an improvement proposal which using the full convolutional neural network to extract road from remote sensing images.The scheme innovatively researches the algorithm principle of the full convolutional neural network and outputs the pre-graded GF-2 images in a certain size.Then,the output images and the corresponding labels are input into the improved full convolutional neural network.At last,a road extraction image with higher recognition accuracy is obtained by combining residual unit and increasing the number of network layers.Experiments show that the effect on road extraction of GF-2 satellite images is improved in the same sample,the integrity and accuracy of the road are also improved.