The invention discloses a high-resolution
remote sensing image road extraction method based on combination of
deep learning and a multi-dimensional attention mechanism. The method comprises the following steps: extracting
remote sensing image road information by adopting a full
convolutional neural network UNet; a multi-dimensional attention module is combined with a coding part of the UNet network, so that a road feature map transmitted to a decoding part has higher feature expression capability; a multi-level
feature fusion mode is adopted, feature information of different levels is obtained in each layer in the decoding stage, and a transmitted feature map has texture information and
semantic information so as to optimize the expression ability of the feature map; a user can observe an extraction result of a high-resolution
remote sensing image returned by a
satellite in real time by accessing a Web front end of node.js based on a
server. According to the scheme, high-accuracy remote sensing image road information is extracted, the image subjected to
convolution training has higher expression ability due to introduction of the multi-dimensional attention module and the multi-level
feature fusion method, and compared with a general
deep learning method, the remote sensing image road extraction accuracy is improved. Meanwhile, the self-feedback mechanism of the
deep learning network enables the extraction process to be more intelligent and automatic, and adaptive adjustment can be performed on images of different road scales in different regions to obtain optimal road image information, so that the method has very high practical value and popularization value.