Remote sensing image semantic segmentation method based on parallel cavity convolution

Abstract

The invention discloses a remote sensing image semantic segmentation method based on parallel cavity convolution, and relates to the technical field of remote sensing images, and the method comprises the following steps: obtaining a high-resolution remote sensing image in advance, and carrying out the slicing, normalization and standardization of the high-resolution remote sensing image, and obtaining a source high-resolution remote sensing image; initializing a low-layer network of a feature extraction network resnet101 based on a resnet101 parameter pre-trained on an ImageNet, constructing a parallel cavity convolutional network, and extracting a shallow-layer feature of a source high-resolution remote sensing image; inputting the shallow layer features into a parallel dilated convolutional network to obtain multi-scale information, and fusing the multi-scale information; and fusing the fused features with the shallow features again, and repairing image-level information by using a full-connection conditional random field to obtain a semantic segmentation result. Under the condition that extra parameters are not increased, the receptive field of convolution is expanded, and compared with standard convolution achieving the same receptive field, the parallel cavity convolution method can save more video memories.

Description

technical field [0001] The invention relates to the technical field of remote sensing images, in particular to a method for semantic segmentation of remote sensing images based on parallel hole convolution. Background technique [0002] With the maturity of satellite remote sensing technology, the advancement of commercialization, and the encouragement and promotion of governments around the world, satellite remote sensing has developed rapidly and has been applied in more and more fields. Semantic segmentation of remote sensing images is an important part of satellite remote sensing applications. Semantic segmentation of remote sensing images is widely used in urban planning, road planning, object extraction, and even pattern recognition in autonomous driving. Improving the accuracy of semantic segmentation is of great significance in the processing of remote sensing images. [0003] The ground object information in remote sensing images is complex and diverse. In order t...

AI Technical Summary

Problems solved by technology

These methods are all based on standard convolution, and the receptive field of standard convolution has limitations. Therefore, expanding the receptive field has important research and application value for semantic segmentation of remote sensing images.

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