基于深度学习的高分六号影像水体自动提取

为了探究高分六号(GF-6)卫星多光谱相机(PMS)影像提取水体的潜力,分别构建全卷积神经网络(FCN-8s)、U-Net及U-Net优化(VGGUnet1、VGGUnet2)4种神经网络进行了水体提取研究.基于水体提取结果对比分析,确定优选模型为VGGUnet1;提出基于组合损失函数FD-water loss(focal-dice-water loss)的VGGUnet1网络模型,并与归一化差分水指数(norma-lized water index,NDWI)阈值法、最大似然分类法、支持向量机分类法等方法比较.结果表明:基于FD-water loss损失函数的VGGUnet1网络模型能有效提取水体目标,增强小面积水体识别能力,减少水体错分、漏分现象,提高水体提取效果.可见全卷积神经网络在GF-6遥感影像水体提取方面具有可行性,为后续该领域的进一步研究应用提供了参考.

Automatic Water Extraction from GF-6 Image Based on Deep Learning

In order to study the potential of water extraction from multispectral camera (PMS) images of GF-6, four kinds of neural networks, including full convolutional neural network (FCN-8s), U-Net and U-Net optimization (VGGUnet1, VGGUnet2), were constructed for water extraction studies. Based on the water extraction results, the best model was determined as VGGUnet1; then a VGGUnet1 network model based on the combined loss function Focal-Dice-Water loss (FD-Water loss) was proposed. Compared with the normalized water index (NDWI) threshold method, maximum likelihood classification method, and support vector machine classification method, the results show that the VGGUnet1 network model based on the FD-Water loss function can effectively extract the water body target, enhance the recognition ability of the water body in a small area, reduce the phenomenon of misdivision and leakage of the water body, and improve the extraction effect of the water body. It is concluded that the full convolutional neural network is feasible in water extraction of GF-6 remote sensing images, which provides a reference for further research and application in this field.