基于Attention U-Net的陆地卫星影像云检测

Cloud Detection of Landsat Images Based on Attention U-Net

云检测是提高遥感影像利用率和应用范围的有效措施。然而,现有云检测算法大多存在以下两个问题:冰、雪等复杂下垫面与云不易区分;需要大量人工标记好的云样本对模型进行训练。为提高影像云识别精度,提出了一种基于Attention U-Net的陆地卫星影像云检测算法。首先,利用卷积操作在编码模块提取云的浅层特征;然后,利用反卷积、跳跃连接和注意力机制在解码模块进一步挖掘云特征;最后,利用少量公开的陆地卫星影像云样本数据进行训练,实现端到端的陆地卫星影像像素级云检测。实验结果表明,与传统的机器学习算法相比,所提算法的总体检测精度更高,薄云和云阴影的误检率和漏检率更低。

Cloud detection is an effective measure to improve the utilization rate and application range of remote sensing images.However,most of the existing cloud detection algorithms face with two problems:the difficulty in distinguishing complex underlying surfaces like ice and snow from cloud and the requirement of a large number of manually labeled cloud samples for model training.To improve the performance of cloud recognition,we propose a cloud detection algorithm based on Attention U-Net for Landsat images.Firstly,convolution operation is conducted to extract the shallow features of cloud in coding modules.Then,deconvolution,jump connection and attention mechanism are integrated to mine deeper cloud features in decoding modules.Finally,a small number of public Landsat image cloud samples are used for training to achieve end-to-end pixel-level cloud recognition.Compared with traditional machine learning algorithms,experimental results indicate that the proposed algorithm has higher overall accuracy,lower false detection rate and missed detection rate of thin cloud and shadow.