Remote sensing image change detection method, electronic equipment and storage medium

A remote sensing image and change detection technology, applied in the field of image processing, can solve the problems that deep learning cannot be directly expressed and described, and the manpower and time costs of deep learning are high, so as to achieve the effect of reducing manpower and time costs and improving accuracy

Pending Publication Date: 2020-11-24
THE CHINESE UNIV OF HONG KONG SHENZHEN
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Problems solved by technology

[0005] The main purpose of the present invention is to provide a remote sensing image change detection method, electronic equipment and storage medium, aiming to solve the problem that deep learning

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  • Remote sensing image change detection method, electronic equipment and storage medium
  • Remote sensing image change detection method, electronic equipment and storage medium
  • Remote sensing image change detection method, electronic equipment and storage medium

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[0032] In order to make the object, features and advantages of the invention more obvious and understandable, the technical scheme in the embodiment of the invention will be clearly and completely described below with reference to the drawings in the embodiment of the invention. Obviously, the described embodiment is only a part of the embodiment of the invention, but not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by the skilled person without creative labor belong to the scope of the present invention.

[0033] see also Figure 1 Which is a remote sensing image change detection method, comprising: S1, acquiring a remote sensing image; S2, inputting the remote sensing image into a pre-generated deep learning model; The depth model consists of automatic noise reduction encoder, cascade layer, full connection layer and logistic regression layer. S3, receiving the change detection result graph generated by the deep learning mo...

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Abstract

The invention discloses a remote sensing image change detection method. The method comprises the following steps: acquiring a remote sensing image; inputting the remote sensing image into a pre-generated deep learning model, wherein the depth model comprises an irregular image object depth feature extraction module and a depth feature fusion classification module, the depth feature extraction module is generated by pre-training an unsupervised stack type noise reduction automatic encoder, and the depth feature fusion classification module is composed of a pre-trained noise reduction automaticencoder, a cascade layer, a full connection layer and a logistic regression layer; receiving a change detection result graph generated by the deep learning model; and outputting a detection result according to the transformation detection result graph. In the training process of the learning model, a large amount of labeled data does not need to be used for training, so that the labor and time cost of deep learning is reduced, the edge and shape information of the irregular object can be kept by the deep learning model, and the depth features of the irregular image object can be expressed anddescribed.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a remote sensing image change detection method, electronic equipment and a storage medium. Background technique [0002] Remote sensing earth observation technology has become an important means of dynamic detection of land use / cover change. High-resolution remote sensing image change detection is to process and analyze multiple remote sensing images covering the same area acquired at different times to realize dynamic detection of changes in surface features. [0003] The current detection methods mainly include pixel-level change detection and object-oriented change detection. Since pixel-level change detection can reduce salt and pepper noise and speckle noise in the results, object-oriented change detection has been widely used, but object-oriented transformation The detection method is not highly automated, and still faces the problem of feature selection and sampl...

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Application Information

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IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0002G06T2207/10032G06T2207/20081G06T2207/20084G06T7/10
Inventor 张效康潘文安
Owner THE CHINESE UNIV OF HONG KONG SHENZHEN
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