Remote sensing image segmentation method based on feature recalibration dual-channel residual network

Abstract

The invention discloses a remote sensing image segmentation method based on a feature recalibration dual-channel residual network, and the method comprises the steps: S1, obtaining a to-be-detected remote sensing image, and carrying out the normalization processing of the to-be-detected remote sensing image, so as to obtain a normalized image; S2, performing random window sampling on the normalized image to obtain a plurality of sample data with set sizes; S3, segmenting the plurality of sample data by adopting a pre-established dual-channel residual network remote sensing segmentation model to obtain segmented remote sensing images. The training process of the dual-channel residual network remote sensing segmentation model is that the dual-channel residual network is trained according toa known remote sensing image to obtain the dual-channel residual network remote sensing segmentation model.

Description

technical field [0001] The solution relates to a remote sensing image recognition method, in particular to a remote sensing image segmentation method based on a feature recalibration dual-path residual network. Background technique [0002] At present, image segmentation methods based on neural network models are developing rapidly, and new image data can be segmented with trained neural networks, which are better than traditional segmentation methods in terms of segmentation accuracy and efficiency. With the development of deep learning, a large number of neural network models continue to appear, and the simple convolution layer can no longer guarantee the convergence speed of the network and the accuracy of image segmentation. The existing fully convolutional neural network adopts an end-to-end, pixel-to-pixel structure, which avoids the problem of repeated storage and calculation convolution due to the use of pixel blocks, but the network is not sensitive enough to the de...

AI Technical Summary

Problems solved by technology

[0004] In view of the above-mentioned deficiencies in the prior art, the remote sensing image segmentation method based on the feature recalibration dual-path residual network provided by the present invention solves the problem of low segmentation accuracy of the existing remote sensing image

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