Remote sensing image change detection algorithm based on coupling discriminant feature self-learning network

A remote sensing image and change detection technology, applied in the field of remote sensing image change detection algorithm, can solve the problems of large difference in accuracy of remote sensing image change detection and high label data requirements, and achieve strong practical application value, broad application space, and reduce labor costs Effect

Pending Publication Date: 2021-03-19
BEIJING RES INST OF URANIUM GEOLOGY
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Problems solved by technology

[0005] The purpose of the present invention is to propose a remote sensing image change detection algorithm based on a coupled discriminant feature self-learning network, which is use

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  • Remote sensing image change detection algorithm based on coupling discriminant feature self-learning network
  • Remote sensing image change detection algorithm based on coupling discriminant feature self-learning network
  • Remote sensing image change detection algorithm based on coupling discriminant feature self-learning network

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Embodiment Construction

[0075] Below in conjunction with accompanying drawing and specific embodiment the method that the present invention designs is described further:

[0076] In the present invention, a coupling discriminant feature self-learning network is proposed. The network can be divided into two parts, denoted as G and F. Its structure is a typical fully connected network, and the input end of the network is the original feature of each pixel of the remote sensing image. , we use the pixels in the local area around the pixel as its original features (p,p∈R s×1 ), this feature contains the local neighborhood information of the central pixel. The output of the network is:

[0077]

[0078]

[0079] Among them, I 1 and I 2 are two remote sensing images to be detected, and the size of each remote sensing image is, M×N, where M is the width of the remote sensing image, and N is the height of the remote sensing image. g( ) and f( ) represent the mapping functions of networks G and F, a...

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Abstract

The invention provides a remote sensing image change detection algorithm based on a coupling discrimination feature self-learning network. According to the algorithm, a coupling discrimination featureself-learning network is mainly designed, the network performs feature extraction on a to-be-detected image through two sub-networks, an established coupling training model can obtain a coupling feature space, the discrimination ability of features in the space is enhanced, generation of a more distinct difference graph is facilitated, and the detection accuracy is improved. An accurate change detection result is obtained. The method does not need label data, is not limited by data types, and has a wide application space. The effectiveness of the algorithm is verified on a public data set.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a remote sensing image change detection algorithm based on a coupled discriminant feature self-learning network. [0002] technical background [0003] Remote sensing images are currently used more and more widely, among which the use of remote sensing images for change detection is a very important application field, such as: land and resources survey, urban development monitoring, post-disaster situation analysis, natural resource statistical analysis, etc. The main difficulty in remote sensing image change detection is the difference in remote sensing image representation caused by different time, different sensors, and different data types, which will seriously interfere with the discovery of important change areas in remote sensing images. [0004] The key issue in change detection is how to properly represent the features of different remo...

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Application Information

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IPC IPC(8): G06T7/00G06K9/46G06N3/04G06N3/08
CPCG06T7/0002G06N3/084G06T2207/10032G06V10/40G06N3/045
Inventor 秦凯陈璞花朱玲孙杰杨越超崔鑫李明
Owner BEIJING RES INST OF URANIUM GEOLOGY
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