Remote sensing image semantic segmentation method, system and equipment and storage medium

Abstract

The invention discloses a remote sensing image semantic segmentation method, system, equipment and a storage medium, belongs to the field of image processing, and aims to solve the technical problems of low semantic segmentation precision and low segmentation efficiency. The method comprises the following steps: constructing, training and testing a network, wherein the network is specifically a deep semantic segmentation network of an encoder-decoder structure constructed by a Pytorch deep learning framework; performing network training based on the remote sensing image data sample set; and taking a to-be-measured remote sensing image as network input to obtain a segmentation result of the remote sensing image. On one hand, model parameters are reduced through a bottleneck type module, depth separable convolution, asymmetric convolution, convolution with holes and the like, the calculation complexity is reduced, and the time of remote sensing image semantic segmentation is shortened; on the other hand, the semantic segmentation precision is improved through multi-scale feature aggregation and a mixed attention module, so that the provided remote sensing image semantic segmentation network can accurately and efficiently realize the semantic segmentation of the remote sensing image.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a remote sensing image semantic segmentation method, system, equipment and storage medium. Background technique [0002] The purpose of image segmentation is to mark each pixel as a category. For remote sensing images, the pixels are marked as a type of ground features, such as buildings, water bodies, roads, cultivated land, vehicles, and so on. Image semantic segmentation is developed from traditional image segmentation methods. Traditional image segmentation methods (threshold method, k-Means clustering method, region method, edge detection method) only care about finding the boundary contours of ground objects, and do not care about the objects they belong to. Semantic segmentation not only needs to accurately find the outline of the feature, but also needs to accurately judge the category to which the feature belongs, that is, to give its semantics. Thanks to the rapid develop...

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Problems solved by technology

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the object of the present invention is to provide a remote sensing image semantic segmentation method, system, equipment and storage medium, aiming to solve the defects of low semantic segmentation accuracy and low segmentation efficiency in the prior art technical problem

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