Aerial Image Segmentation Method Based on Global and Multi-Scale Fully Convolutional Networks

Abstract

The invention discloses an aerial image segmentation method based on a global and multi-scale full convolutional network, the steps of which are: constructing a global and multi-scale full convolutional network; generating a training set; training the global and multi-scale full convolutional network; The segmented aerial image is input to the trained global and multi-scale fully convolutional network for binary segmentation to generate a segmentation mask. The invention uses the global and multi-scale full convolutional network to segment the aerial image, and embeds the global module and the multi-scale module in the global and multi-scale full convolutional network to extract a finer segmentation mask, which has strong robustness and High precision.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an aerial image segmentation method based on a global and multi-scale full convolution network in the technical field of image segmentation. The invention can be used to detect the building target from the high-resolution aerial image and segment the area where the building is located from the image. Background technique [0002] With the continuous development of today's society, urban construction planning has become a hot topic of concern. With the increasing demand for buildings, more buildings have increased the difficulty of urban infrastructure construction, such as traffic route planning, drainage system planning, and convenience facility planning. The detection and segmentation of buildings in aerial images can help the construction planning department to detect and segment urban buildings and carry out urbanization infrastructure construction. However, ...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the convolutional encoding-decoding network structure in the convolutional encoding-decoding network relies on multi-layer convolutional layers. Due to the limitation of the size of the convolution kernel, only local information can be extracted, and there is a lack of global information, resulting in Segmentation accuracy is low

The shortcomings of this method are: only the relationship between the encoding and decoding parts of the full convolutional network is considered, the different effects of each convolutional layer on the final prediction in the decoding part of the full convolutional network are not considered, and the multi-scale features are not considered, resulting in It is difficult to identify similar objects of different sizes in the image, and the simplicity and efficiency of the network are not considered, resulting in low network segmentation performance

Images