Remote sensing image semantic segmentation method based on migration VGG network

Abstract

The invention discloses a remote sensing image semantic segmentation technology based on a VGG network. The method comprises the following steps: 1, randomly cutting the high-resolution remote sensingimage for training and the corresponding label image into small images; dividing the network structure into an encoding part and a decoding part; adopting the depooling path and the deconvolution path to double the resolution of the coded information; carrying out channel connection on a characteristic image and a result of cavity convolution, recovering the characteristic image to an original size through deconvolution upsampling, inputting an output label image into a PPB module for multi-scale aggregation processing, and finally, updating network parameters in a random gradient descent mode by taking cross entropy as a loss function; inputting the small images formed by sequentially cutting the test images into a neural network to predict corresponding label images, and splicing the label images into an original size. According to the technical scheme, the segmentation precision of the model is improved, the complexity of the network is reduced, and the training time is saved.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a remote sensing image semantic segmentation method based on a migration VGG network. Background technique [0002] Semantic segmentation is an important issue of general concern in areas such as unmanned driving, medical image analysis, and geographic information systems. Semantic segmentation is to allow the computer to segment according to the content of the image. Segmentation is to segment different objects in the image from the pixel level, label each pixel in the original image, and classify it into different labels. The accuracy of segmentation is It contains the understanding of the information in the image. Remote sensing images have the characteristics of complex imaging, high picture pixels, and large amount of information. Therefore, how to use artificial intelligence technology to quickly and accurately extract useful information from remote sensing images i...

AI Technical Summary

Problems solved by technology

Compared with FCN and SegNet, U-Net has a more symmetrical encoding and decoding structure, and the skip connection from encoding to decoding part helps the recovery of position information, but also makes the network structure complex and requires more training time

The above network structure often uses pooling to increase the receptive field, but pooling will reduce the spatial resolution when increasing the receptive field.

Although dilated convolutions are used to expand the receptive field and avoid loss of resolution, and the use of convolutions with different dilated rates can capture information of different scales, but dilated convolutions use sparse sampling to lose local information, making long-distance information lack of relevance

In semantic segmentation, a large receptive field can provide more global information, but ignore local information

Images