High-resolution remote sensing image semantic segmentation method based on supervised self-attention network

Abstract

The invention discloses a high-resolution remote sensing image semantic segmentation method based on a supervised self-attention network, and the method comprises the following steps: constructing a remote sensing image semantic segmentation network which comprises a basic feature extraction module, a channel self-attention module, a category supervised self-attention module, a space supervised self-attention module, and an advanced feature fusion module; obtaining remote sensing images shot by an unmanned aerial vehicle or a satellite as a training set, and training the remote sensing image semantic segmentation network by using the training set to obtain a trained network model; and segmenting the to-be-segmented high-resolution remote sensing image by using the trained network model to obtain a target object in the to-be-segmented high-resolution remote sensing image. The method has the advantage that the overall precision of remote sensing image semantic segmentation is improved.

Description

Technical field [0001] The present invention belongs to the field of image segmentation, in particular to a high-resolution remote sensing image semantic segmentation method based on a supervised self-attention network. Background [0002] With the continuous development of satellite remote sensing technology, it has become easy to obtain large-scale high-resolution remote sensing images, and detailed observations of surface features can be obtained through airborne sensors of drones and satellites, while high-resolution remote sensing images provide richer texture and spatial features, containing rich semantic information. Therefore, semantic segmentation of high-resolution remote sensing images can obtain the shape and classification of different features in remote sensing images, obtain visual semantic segmentation images, replace manual information analysis and mining of remote sensing images, and apply the obtained information to urban planning, environmental monitoring, mil...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems existing in the prior art, the present invention provides a high-resolution remote sensing image semantic segmentation method based on a supervised self-attention network. Technical issues with confusing context

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