Remote sensing image semantic segmentation method based on context information and attention mechanism

Abstract

The invention provides a remote sensing image semantic segmentation method based on context information and an attention mechanism. For a to-be-labeled high-resolution remote sensing image, data preprocessing and data amplification are firstly carried out, and then the amplified data is used for training a remote sensing image semantic segmentation model. In the training process, preliminary features are extracted from the image through a backbone network, then multi-scale features are extracted and fused through a multi-scale context information module, then features of different levels are combined with an attention fusion module, and finally bilinear interpolation up-sampling is directly utilized to obtain a final segmentation result. For an unlabeled image, firstly the unlabeled imageis segmented into small image blocks, the small image blocks are input into a semantic segmentation network to obtain a corresponding result, and then the small image blocks are spliced to obtain a final large-size segmentation result graph.

Description

technical field [0001] The invention belongs to the field of remote sensing image recognition, in particular to a high-resolution remote sensing image semantic segmentation method based on context information and attention mechanism. Background technique [0002] In recent years, with the rapid development of remote sensing technology, high-resolution remote sensing image data has become more and more abundant, and semantic segmentation for remote sensing images has gradually become an important research direction. The semantic segmentation task needs to classify each pixel in the remote sensing image according to the semantics, so as to obtain the segmentation result of the whole image. However, due to the large size of the remote sensing image itself, a remote sensing image often contains a large number of different types of ground objects, such as buildings, vegetation, woodland, cars, etc. Among them, buildings have a variety of shapes, the size of cars is very small co...

AI Technical Summary

Problems solved by technology

However, these methods are designed for natural images, and it is difficult to deal with remote sensing images with complex scenes and large-scale changes.

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