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

A convolutional neural network and remote sensing image technology, applied in the field of semantic segmentation of large-scale remote sensing images, can solve problems affecting use, achieve high precision, and reduce manual workload

Inactive Publication Date: 2018-09-21
ZHEJIANG UNIV
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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

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

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Embodiment Construction

[0039] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0040] Such as figure 1 As shown, the present invention is based on a full convolutional neural network semantic segmentation method for large-scale remote sensing images, including the following steps:

[0041] (1) Remote sensing image fusion and band synthesis.

[0042] In the training of machine learning, the quality and quantity of samples are very important; in practical application scenarios, the number of samples can usually be satisfied by some techniques, but the quality of samples depends entirely on manual labeling. Because it is manually labeled, a certain amount of data that has not been correctly labeled is often included in the training. Such data will inevitably have some negative effects on the final result; in order to minimize the nega...

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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...

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Application Information

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IPC IPC(8): G06T7/11G06T7/90G06N3/04
CPCG06T7/11G06T7/90G06T2207/10032G06N3/045
Inventor 罗智凌岑超尹建伟李莹吴朝晖
Owner ZHEJIANG UNIV
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