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Published： 07 June 2023 ，
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崔宾阁，吴景，李心慧，任广波，路燕.2023.结合深度学习和植被指数的滨海湿地高分二号遥感影像信息提取.遥感学报，27（6）： 1376-1386
Cui B G，Wu J，Li X H，Ren G B and Lu Y. 2023. Combination of deep learning and vegetation index for coastal wetland mapping using GF-2 remote sensing images. National Remote Sensing Bulletin， 27（6）：1376-1386
针对滨海湿地植被光谱特征相似而易被混淆分类的问题，本文提出了结合深度学习和植被指数的滨海湿地信息提取网络MFVNet。该网络以高分辨率遥感影像和典型植被指数为输入，将UNet中的双卷积操作替换为本文提出的增强多尺度特征提取模块，用于捕获不同尺度的上下文特征，并在解码器中融合不同感受野的语义特征图，增强了滨海湿地地物的特征表示。在黄河口滨海湿地高分二号遥感影像上进行了实验，结果表明：（1）深度学习方法的信息提取精度普遍优于传统的机器学习分类方法SVM；相比HRNet等深度语义分割网络，MFVNet在滨海湿地植被类型上取得了更好的信息提取结果；（2）将修正土壤调节植被指数MSAVI、差值植被指数DVI和比值植被指数RVI与高分二号影像拼接对滨海湿地信息提取贡献较大。
The biomass and growth of coastal wetland vegetation vary greatly due to different water and salt conditions in the growing area
and the spectral features of certain vegetation at the peak biomass are highly similar
making it easy for coastal wetland vegetation to be misclassified. In response to this problem
this study proposes a new semantic segmentation network called MFVNet to be combined with vegetation index for the fine mapping of coastal wetlands.
In the proposed MFVNet
an Enhanced Multiscale Feature Extraction (E-MFE) module was first constructed on the basis of atrous convolution and attention mechanism to capture features of different scales adaptively. Then
the E-MFE module was used to replace the double convolution operations in traditional encoder-decoder network architecture
such as UNet. It was also used to merge the semantic features and detailed features of different resolutions to enhance feature representation. Finally
some typical vegetation indices were selected and input into the proposed MFVNet to improve the ability of coastal wetland fine mapping.
The experiments of this study were conducted using GF-2 remote sensing images to study the coastal wetlands of the Yellow River Estuary. Experimental results indicated that the proposed MFVNet achieved good performance with an overall accuracy of 93.89% and a Kappa coefficient of 0.9072. On typical vegetation
such as reeds
spartina alterniflora
tamarix mixed area
and seagrass beds in the Yellow River Estuary
the F1 scores of MFVNet were 0.91
0.87
0.82
and 0.76
respectively
which were better than that of other methods. Moreover
ablation experiments showed that the combination of the E-MFE module and the vegetation index can increase the overall accuracy from 91.46% to 93.89%.
(1) Compared with deep semantic segmentation networks
such as HRNet
MFVNet can more effectively extract vegetation information of coastal wetlands. (2) The proposed EMFE module can adaptively capture features of different scales and improve the overall accuracy
which justified its effectiveness in coastal wetland mapping. (3) The inclusion of vegetation index can enhance the spectral features of coastal wetland vegetation and improve the accuracy of vegetation information extraction
indicating the importance of vegetation index in coastal wetland mapping. (4) Simultaneously splicing modified soil adjusted vegetation index
difference vegetation index
and ratio vegetation index in remote sensing images contributed the most to the extraction of coastal wetland information.
遥感滨海湿地信息提取高分二号深度卷积神经网络MFVNet模型植被指数
remote sensingcoastal wetland information extractionGF-2deep convolutional neural networkMFVNet modelvegetation index
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