The invention belongs to the technical field of
remote sensing, and particularly relates to a
nuclear power plant surrounding ground feature element extraction method based on
deep learning, and the method comprises the steps: 1, making a
nuclear power plant surrounding ground feature
deep learning sample; step 2, based on the HRNet model, constructing a Mama attention residual module, an inner
convolution residual module and an asynchronous
convolution residual module through Mama, inner
convolution and heterogeneous convolution, and constructing an improved HRNet model; 3, training of a U-Net model, a Deeplabv3 model, a HRNet model, a Swin
Transformer model and an improved HRNet model is completed through the sample set training data and the
verification data; 4, evaluating land element extraction results of different
deep learning models; and 5, extracting elements of the surrounding land of the large-scale
nuclear power plant by using the trained improved HRNet model. The method can effectively improve the extraction precision of complex ground feature elements around the
nuclear power plant, remarkably improves the calculation efficiency of the model, and is suitable for efficient monitoring and classification of multiple ground feature elements around the
nuclear power plant in a
large range.