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王国杰，胡一凡，张森，茹易，陈开南，吴梦娟.2022.深度卷积神经网络的遥感影像水体识别.遥感学报，26（11）： 2304-2316
Wang G J，Hu Y F，Zhang S，Ru Y，Chen K N and Wu M J. 2022. Water identification from the GF-1 satellite image based on the deep Convolutional Neural Networks. National Remote Sensing Bulletin， 26（11）：2304-2316
从遥感影像中准确识别水体信息对水资源管理和洪涝灾害监测有重要意义。目前，传统遥感水体识别方法仍存在着不足，难以满足实际应用中的精度要求。近年来，卷积神经网络CNN（Convolutional Neural Network）的快速发展，为高分辨率遥感水体识别提供了新的思路。本文基于高分一号卫星数据，利用DenseNet、ResNet、VGG、HRNet等CNN模型和传统的归一化差异水体指数（NDWI）进行洪泽湖地区不同季节的水体识别，并采用精确度（
P
）、召回率（
R
）、
F
1分数和误判率（
MRate
）等指标来评价各种方法的水体识别能力。在DenseNet经典结构中加入了上采样过程和跳层连接结构，以解决梯度爆炸和梯度消失问题；采用OSTU方法确定NDWI的最优阈值，以降低水体识别的不确定性。得出主要结论如下：（1）所有CNN网络模型的水体识别效果都显著优于传统NDWI方法；例如，NDWI识别的精确度仅为0.779，而所有CNN网络模型的识别精确度均高于0.922。（2）改进后的DenseNet模型有效缓解了梯度爆炸与梯度消失的问题，其水体识别结果在识别精确度
P
（0.960）和误判率（0.041）等方面，明显优于其他CNN模型；同时，修改后的DenseNet模型训练时间更短，且损失函数最低，训练效率远优于其他CNN网络。（3）改进后的DenseNet模型对水体细部特征有很好的识别能力，能够准确识别不同季节中不同形状与颜色的水体。上述结果表明，CNN模型是从卫星影像中识别水体的可靠工具，而改进后DenseNet模型的识别效果尤其显著。
Rapid and accurate identification of water bodies from remote sensing images is of great significance to water resources management and flood disaster monitoring. At present
traditional methods for identifying water bodies from satellite images still have shortcomings
and sometimes the results are not accurate enough to meet the practical needs. Recently
the Convolutional Neural Network (CNN) methods have emerged and been rapidly developed
providing a new idea for a identifying water bodies from satellite images. In this work
the Densely Connected Deep Convolutional Neural Network (DenseNet) is used to identify water bodies in the Hongze Lake area
together with the ResNet
VGG
HRNet networks
and the traditional method of Normalized Difference Water Index (NDWI). We have added the upsampling process and the skip connection structure to its classical structure to improve the performance of the DenseNet network. These methods are applied to the GF-1 satellite images of the Hongze Lake area to identify the water bodies in different seasons. Experiments are conducted to determine the optimal parameters of DenseNet
ResNet
VGG
and HRNet networks for water body identification. Moreover
the OSTU method is used to determine the optimal threshold of NDWI to reduce the uncertainty of threshold determination. Several indices of precision (P)
recall (R)
F1 score
and misclassification rate (MRate) are used to evaluate the performance of these methods. The main conclusions we have reached are as follows: (1) All the CNN models of ResNet
VGG
HRNet
and DenseNet have significantly outperformed the traditional NDWI method; for example
the precision (P) of water identification by using the NDWI method is only 0.779 compared with ground truth; however
it is highly improved to >0.922 by utilizing the CNN models. (2) The modified DenseNet model has effectively alleviated the problems of gradient explosion and disappearance
and the water body identification result is much better than the other CNN models
e.g.
with the best P (0.960) and MRate (0.041). The training efficiency of the modified DenseNet model also appears far better than that of the other CNN models with the shorted training time
and the lowest loss function. (3) The modified DenseNet model shows also a better capability in identifying the fine features of water bodies
even if their shapes and water colors change largely in different seasons. These results have indicated that the CNN models are good tools for identifying water bodies from satellite images
and the modified DenseNet model appears to be the most promising one among them.
卫星遥感水体识别归一化差异水体指数卷积神经网络
satellite imageswater identificationnormalized difference water indexConvolutional Neural Network
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