Remote sensing image segmentation method and device, storage medium and server

Abstract

The invention relates to the technical field of image detection and image classification, and provides a remote sensing image segmentation method, which comprises the following steps: obtaining a target image, and extracting hierarchical characteristics of the target image through each convolutional layer of a deep convolutional neural network; determining target features of the target image basedon the hierarchical features extracted from the convolution layers of the two different scales in sequence; and determining the object category of each object in the target image according to the target feature, and segmenting the target image according to the object category. According to the invention, the convolutional neural network is adopted to extract multi-scale features from shallow to deep; and deep, middle and shallow features are fused in a jumping mode to obtain high-level semantic information and low-level geometric information, and then spatial output is realized through up-sampling, so that the purpose of pixel level segmentation is achieved, and the segmentation precision of the remote sensing image under cloud, strong illumination and other interference factors is improved.

Description

technical field [0001] The invention relates to the technical fields of image detection and image classification, in particular to a remote sensing image segmentation method, device, storage medium, and server. Background technique [0002] Remote sensing image data is also used as the basic data in the three major spatial information technologies of Geographic Information System (GIS), Global Positioning System (GPS), and remote sensing system (RS). Various fields such as environmental monitoring, resource survey, land use, urban planning, natural disaster analysis and military. In recent years, with the development of high-resolution remote sensing satellites, imaging radars, and unmanned aerial vehicle (Unmanned Aerial Vehicle) technology, remote sensing image data has further presented the characteristics of massive, complex, and high-resolution. Accurate extraction of information and data sharing have important research significance and application value. Contents of...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the above technical problems, especially the inaccurate extraction of information from existing remote sensing images, resulting in the inability to accurately segment remote sensing images, the following technical solutions are proposed:

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