Full convolutional neural network-based large-range remote sensing image semantic segmentation method

Abstract

The invention discloses a full convolutional neural network-based large-range remote sensing image semantic segmentation method. The method comprises three stages of data tagging, model training and result prediction; the method performs preprocessing operations of waveband synthesis, image fusion and image segmentation on remote sensing images for characteristics of high precision, large range, multispectral information and the like of the remote sensing images; the richness of samples is improved by applying a data enhancement technique to a training set; and if a data set with a label can be obtained, the data set can be used for training a model firstly, and target data model training is initialized to reduce the workload of manual labeling. For improving the accuracy of a result, themethod performs overlapping grid division on the images, performs prediction, splices prediction result images in sequence, and performs median filtering to reduce noises and unsmooth parts in the images; and finally, relatively high accuracy is achieved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image recognition and deep learning, and in particular relates to a method for semantic segmentation of large-scale remote sensing images based on a fully convolutional neural network. Background technique [0002] Remote sensing is to image the earth in a specific electromagnetic spectrum through the sensors on the satellite. It is a technology developed on the basis of aerial photography technology; Multi-level and multi-perspective observation is an important means to obtain environmental information and earth resources. [0003] Remote sensing technology is one of the important symbols to measure a country's scientific and technological level and comprehensive strength. my country has always attached great importance to the development of remote sensing technology, which has made remote sensing technology develop rapidly; at present, remote sensing technology has been widely used in oce...

AI Technical Summary

Problems solved by technology

[0009] In high-resolution satellite remote sensing images, specific targets in the image can be marked by image semantic segmentation technology, so as to extract specific information in remote sensing images, such as identification and division of houses, identification of road networks, vegetation separation, etc. etc.; these are the basic information extraction of remote sensing images, but more other applications need to be carried out on this basis, and the accuracy of basic information extraction directly affects the subsequent use

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