Remote sensing image ground object classification method based on depth learning semantic segmentation network

Abstract

The invention discloses a remote sensing image ground object classification method based on a depth learning semantic segmentation network. Firstly, pixel-by-pixel labeling is carried out on various ground objects in the remote sensing image, and a remote sensing ground object labeling image database is constructed as a training label. A method for constructing a multi-scale feature map group based on structural feature is designed, the feature group and the original image are combined as the input of the depth learning network, in addition, the invention designs an improved network structureof the full convolution network according to the deeplab algorithm, trains the parameters through convolution and deconvolution, finally overlaps and segments the wide remote sensing images, and combines the classification results to obtain the final wide remote sensing image land object classification results. The invention also discloses an improved network structure of the full convolution network according to the deeplab algorithm. High-resolution remote sensing images can be efficiently and quickly achieved pixel-level classification of various objects, simplifying the complex process oftraditional classification methods, and achieving good segmentation and classification results.

Description

technical field [0001] The invention belongs to the technical field of intelligent classification of remote sensing images, and more specifically relates to a classification method of remote sensing ground objects based on a fully convolutional semantic segmentation network under the requirement of ground object interpretation. Background technique [0002] Remote sensing image classification is currently widely used in various military and civilian applications such as land surveying, satellite law enforcement, and regional investigation, and has achieved good application results and has great market development potential. With the gradual increase of satellite load and data volume, in the research of high-precision remote sensing classification, especially when facing large-scale (national or global scale) surface classification, the traditional manual calibration method is difficult to support the explosive growth of tasks and demands. Therefore, it is an important task w...

AI Technical Summary

Problems solved by technology

With the gradual increase of satellite load and data volume, in the research of high-precision remote sensing classification, especially when facing large-scale (national or global scale) surface classification, the traditional manual calibration metho

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