Neural network, building extraction method, medium and computing equipment of remote sensing image

Abstract

The invention discloses a neural network, a building extraction method of a remote sensing image, a medium and a computing device. The disclosed neural network is used for building extraction of remote sensing images, including: the input layer in the VGG network, the first to fifth convolutional layers, and the first to fourth pooling layers; the first single-scale fusion layer, the second The input end of a single-scale fusion layer is connected to the output end of the first convolutional layer; the second to fifth single-scale fusion layers, and the input ends of the second to fifth single-scale fusion layers are respectively connected to the second to fifth volumes The output end of the product layer; the first to fourth upsampling layers, the input ends of the first to fourth upsampling layers are respectively connected to the output ends of the second to fifth single-scale fusion layers; multi-scale splicing fusion layer, multi-scale The input end of the splicing and fusion layer is connected to the output ends of the first single-scale fusion layer and the first to fourth upsampling layers; an output layer. The disclosed neural network can effectively process buildings with dense distribution and various scales, and improve the precision of automatic building extraction.

Description

technical field [0001] The invention relates to the fields of neural network and image processing, in particular to a neural network, a method for extracting buildings from remote sensing images, a medium and a computing device. Background technique [0002] With the rapid development of sensor technology, the spatial resolution of remote sensing images has been continuously improved. Inspired by deep learning algorithms in the field of computer vision, scholars currently use convolutional neural networks to implement semantic segmentation tasks for remote sensing images. Although some cutting-edge methods have achieved good results in semantic segmentation tasks of remote sensing images, they have not considered some characteristics of remote sensing images themselves. First of all, in conventional computer vision semantic segmentation tasks, there are generally only a few to dozens of targets on the image to be detected, and the distribution of targets is relatively loose...

AI Technical Summary

Problems solved by technology

However, there is no effective method in the prior art that can make full use of convolutional neural networks to extract feature information of buildings of different scales in remote sensing images.

Images