天绘一号卫星遥感影像云雪检测的ResNet与DeepLabV3+综合法

A cloud and snow detection method of TH-1 image based on combined ResNet and DeepLabV3+

云和雪的检测是卫星遥感影像处理过程中的一部分,也是对其进行后续分析和解译等应用的关键步骤。本文提出了结合ResNet和DeepLabV3+的全卷积神经网络云雪检测方法。采用ResNet50骨干网络,根据云和雪在天绘一号遥感影像上的特点优化DeepLabV3+网络模型,并采用ELU激活函数、Adam梯度下降法以及Focal Loss损失函数来加快收敛速度、提高分割精度。通过天绘一号卫星云雪影像数据集对网络进行训练并测试,试验结果表明,本文方法与传统Otsu法相比,稳健性更强,在检测精度上优于FCN-8s与DeepLabV3+,速度上优于DeepLabV3+,能推广用于不同来源的遥感影像,具有较好的应用前景。

Cloud and snow detection is an important part of satellite remote sensing image processing,and also a key step for its following analysis and interpretation.In this paper,a simultaneous cloud and snow detection method for satellite remote sensing images based on ResNet and DeepLabV3+fully convolutional neural network is proposed.The ResNet50 backbone is used,and the DeepLabV3+network structure is optimized and improved according to the characteristics of cloud and snow on TH-1 remote sensing image.The ELU activation function,Adam gradient descent method and Focal Loss function are used to speed up convergence and improve segmentation accuracy.The network is trained and tested with the cloud and snow image dataset of TH-1 satellite.The experimental results show that it has strong robustness compared with Otsu method,and the detection accuracy of proposed method exceeds FCN-8s and original DeepLabV3+network,meanwhile the detection speed of proposed method is faster than original DeepLabV3+,which can be applied to a variety of different remote sensing images through some adjustment and has favorable application prospects.