基于深度神经网络的河流遥感图像分割方法研究OA北大核心CSTPCD
Research on river remote sensing image segmentation method based on deep neural network
为解决河流遥感图像分割效果较差且交并比较低的问题,提出了基于深度神经网络的河流遥感图像分割方法.通过对高空间分辨率的河流遥感图像数据集的分析,预处理河流遥感图像,解决数据集中存在的弱标签问题;采用卷积编码-解码网络构建深度神经网络的特征提取模型,并运用KNN算法实现河流遥感图像的高精度分割;最后以重庆市嘉陵江2022 年河流遥感图像为例进行验证.实验结果表明:所提方法能够保留分割后的图像细节特征,且图像分割交并比较高,为 0.94.所提方法能够对…查看全部>>
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 feat…查看全部>>
李宗斌
重庆文理学院,重庆 402160
计算机与自动化
河流遥感图像图像分割特征提取残差连接深度神经网络嘉陵江
remote sensing images of riversimage segmentationfeature extractionresidual connectiondeep neural net-worksJialing River
《人民长江》 2024 (7)
73-78,97,7
国家自然科学基金区域创新发展联合基金项目(U22A20102)
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