Water extraction based on dense multi-scale features from remote sensing images
Aiming at the problem of loss of edge detail information and low accuracy in the extraction results of traditional remote sensing image water extraction methods and classical target extraction models based on deep learning,this paper proposes a multi-scale feature dense connection network structure based on deep feature coding and water recognition decoding.Firstly,the ordinary convolution in the deep feature coding structure is used to extract the feature information of the water body in the image,then the dense multi-scale feature module is used to extract the multi-scale features of the water body and retain the global information,and finally the water body in the image is predicted by the water body recognition and decoding structure.Experimental results show that the extraction accuracy of the proposed method is superior to the classical full convolutional neural network model.The pixel accuracy on the test set reaches 98.56％and the intersection over Union reaches 78.91％,effectively preserving the integrity of the water body and the detailed edge information,and realizing the fine extraction of the water body.
remote sensing imagedeep learningwater extractiondense connection networkexpansion convolutiondense multi-scale features
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