基于卷积神经网络的SAR图像水体提取

A method for water body extraction in SAR image using CNN

基于SAR图像的水体提取在洪涝监测等领域应用广泛。基于阈值分割的水体提取方法容易将山体阴影分类为水体进行错误提取,传统机器学习的分类方法需要人工提取有效的特征,低效耗时。提出了基于卷积神经网络的SAR图像水体提取方法,首先对SAR图像进行分块处理,通过基于图像块的多层卷积操作和池化操作自动学习SAR图像特征,最后利用Sigmoid分类器对所提取的特征进行水体和非水体的分类,实现水体的提取。通过基于Sentinel-1A获取的SAR数据的实验验证了所提方法的有效性,水体提取的召回率和精确率均可达到99%,并且性能优于OTSU阈值方法和基于纹理特征的SVM方法。该方法克服了山体阴影对水体提取的影响,并且其自动学习特征的能力可以实现水体的高效提取。

Water body extraction based on SAR images is widely used in flood monitoring and other fields. The water body extraction method based on threshold segmentation is easy to classify mountain shadows as water bodies for wrong extraction. Traditional machine learning classification methods require manual extraction of effective features, which is inefficient and time-consuming. This paper proposes a SAR image water extraction method based on convolutional neural network. First, the SAR image is divided into blocks, and the SAR image features are automatically learned through multi-layer convolution and pooling operations based on image blocks. Finally, the Sigmoid classifier is used to classify the pixels into water and non-water to realize the extraction of water body. Experiments based on SAR data obtained by Sentinel-1 A verify the effectiveness of this method. The recall rate and accuracy rate of water extraction can reach 99%, and the performance is better than the OTSU threshold method and the SVM method based on texture features. This method overcomes the influence of mountain shadows on water body extraction, and its ability to automatically learn features can achieve efficient water body extraction.