Aerial image segmentation method based on global and multi-scale full convolutional networks

Abstract

The invention discloses an aerial image segmentation method based on global and multi-scale full convolutional networks. The method comprises the following steps: constructing the global and multi-scale full convolutional networks; generating a training set; training global and multi-scale full convolutional networks; and inputting a to-be-segmented aerial image into the trained global and multi-scale full convolutional network for binary segmentation, and generating a segmentation mask image. According to the method, the aerial image is segmented by using the global and multi-scale full convolutional networks, and the global module and the multi-scale module are embedded into the global and multi-scale full convolutional networks, so that a finer segmentation mask is extracted, the robustness is high, and the segmentation precision is high.

Description

Technical field [0001] The present invention belongs to the field of image processing technology, and further relates to an aerial image segmentation method based on global and multi-scale full convolutional networks in the field of image segmentation technology. The invention can be used to detect the building target from the high-resolution aerial image and segment the building area 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 transportation route planning, drainage system planning, and convenience facilities planning. Building detection and segmentation in aerial images can help the construction planning department to detect and segment urban buildings and carry out urbanization infrastructure construction. However, due to the ric...

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

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