Research on river remote sensing image segmentation method based on deep neural network
To solve the problem of poor segmentation effects and low intersection and union ratio of river remote sensing images,a river remote sensing image segmentation method based on the deep neural network was proposed.By analyzing the river remote sensing image data set with high spatial resolution,the river remote sensing image was preprocessed to solve the weak label prob-lem in the data set;the convolutional coding-decoding network was used to construct a feature extraction model of deep neural network,and the KNN algorithm was used to realize the high-precision segmentation of river remote sensing images;finally,the remote sensing images of the Jialing River in Chongqing City in 2022 was taken as an example for verification.The experimental results showed that the proposed method can preserve the detailed features of the segmented image,and the intersection and union ratio of image segmentation was high,which was 0.94.The proposed method can achieve high-precision segmentation of river re-mote sensing images,providing technical support for water resource management and environmental protection.
remote sensing images of riversimage segmentationfeature extractionresidual connectiondeep neural net-worksJialing River
国家科技期刊平台
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