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Remote sensing image change detection method based on object-level semi-supervised CV model

A remote sensing image and change detection technology, applied in the field of remote sensing, can solve the problems of low computational efficiency, high-resolution remote sensing images with salt and pepper noise, etc., to achieve the effect of improving overall efficiency, promoting the development of automation and intelligence, and improving learning performance

Active Publication Date: 2019-07-30
湖北省水利水电科学研究院
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Problems solved by technology

[0006] The purpose of the present invention is to provide a remote sensing image change detection method based on an object-level semi-supervised CV model, which can effectively solve the problems that the CV model change detection algorithm has more salt and pepper noise and l

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[0042] The specific implementation of the remote sensing image change detection method based on the object-level semi-supervised CV model according to the present invention will be described in detail below with reference to the accompanying drawings.

[0043]

[0044] In this embodiment, taking land cover change detection as an example, the remote sensing image change detection method based on the object-level semi-supervised CV model is described.

[0045] Such as figure 1 As shown, the remote sensing image change detection method based on the object-level semi-supervised CV model provided by this embodiment includes the following steps:

[0046] 1) Obtain remote sensing images of each time phase, and perform preprocessing of image registration and relative radiation correction on remote sensing images of each time period.

[0047] The method of preprocessing remote sensing images at each time period is to use the image of the first time phase as a reference, and perform geometric r...

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Abstract

The invention provides a remote sensing image change detection method based on an object-level semi-supervised CV model. The method comprises the steps of preprocessing remote sensing images of all time phases; superposing the images, and performing multi-scale segmentation to form homogeneous image objects; calculating a change intensity characteristic of each image object, and carrying out characteristic mapping on the pixels to obtain a change intensity characteristic graph; performing initial clustering on the object change intensity characteristics to obtain a membership matrix; calculating a category marking information entropy of each object by adopting an information entropy measurement method, and then carrying out category initial marking to generate category marking knowledge; taking the change intensity feature map as an input feature, introducing category marking knowledge into a CV model, constructing an energy functional considering the category marking knowledge, and establishing an object-level semi-supervised CV model; and through solving an Euler equation corresponding to the energy functional, constructing an energy constraint, guiding the rapid evolution of thecurve to the target contour, and realizing the automatic change detection of the remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing, and in particular relates to a remote sensing image change detection method based on an object-level semi-supervised CV model. Background technique [0002] Remote sensing change detection can provide large-scale, long-term, periodic surface change information on the earth's surface, and has become an effective means of monitoring surface change information. With the rapid development of aerospace technology, the spatial resolution, spectral resolution, and temporal resolution of remote sensing image data are getting higher and higher. Remote sensing images with high spatial resolution can provide rich ground object details and spatial information, providing a sufficient data source for change detection. How to intelligently, quickly and accurately extract change information from remote sensing images has become an important research content of change detection. [0003] According to the...

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Application Information

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IPC IPC(8): G06T7/00G06T7/30
CPCG06T7/0002G06T2207/10032G06T2207/20192G06T2207/30181G06T7/30
Inventor 张效康史文中吕志勇
Owner 湖北省水利水电科学研究院
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