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.