Remote sensing image classification method based on multi-scale depth features

A technology of remote sensing images and depth features, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of high-resolution remote sensing images being highly complex, redundant, and unable to accurately describe complex objects and objects.

Inactive Publication Date: 2019-08-02
BEIJING NORMAL UNIVERSITY
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Problems solved by technology

Note that traditional remote sensing image features are usually defined by human experience and there is a large degree of linear correlation and redundancy
Although in recent years, scholars have au

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  • Remote sensing image classification method based on multi-scale depth features
  • Remote sensing image classification method based on multi-scale depth features
  • Remote sensing image classification method based on multi-scale depth features

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[0045] The technical scheme is described in detail in combination with graphics.

[0046] In the image classification method based on multi-scale depth features of the present invention, firstly, the algorithm can automatically establish a multi-scale image pyramid. Secondly, with the help of traditional convolutional neural network algorithms, this algorithm can automatically extract high-level image features using self-learning algorithms. Finally, the multi-scale depth image features and spectral features are fused to improve the classification accuracy of remote sensing images. The following is an example of multi-scale depth feature extraction and remote sensing classification algorithm. The process is as follows: figure 1 As shown, the specific implementation steps are as follows:

[0047] 1. Spectral dimensionality reduction

[0048] Using the principal component analysis (PCA) algorithm, the original remote sensing image data is reduced to three spectral bands while...

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Abstract

The invention belongs to the technical field of remote sensing image classification, and particularly relates to a remote sensing image classification method based on multi-scale depth features. Firstly, the algorithm can automatically establish multi-scale image pyramid; secondly, by means of a traditional convolutional neural network algorithm, the algorithm can automatically extract high-levelimage features by means of a self-learning algorithm; and finally, the multi-scale depth image features are fused with the spectral features, thereby improving the remote sensing image classificationprecision. According to the multi-scale deep neural network algorithm, a multi-scale image pyramid can be automatically constructed for an input remote sensing image, and then a multi-scale training sample is extracted and used for extracting multi-scale image spatial features. The algorithm has the following two advantages: 1) deep stable and effective image features can be automatically extracted, and 2) the multi-scale feature learning method can effectively describe the multi-scale effect of a complex ground object target in an image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image classification, and in particular relates to a remote sensing image classification method based on multi-scale depth features. Background technique [0002] The emergence and development of remote sensing technology as a data acquisition method that can quickly obtain the real situation of a large area of ​​the ground without directly contacting the ground target reflects the improvement of human beings' ability to perceive geographical space and the earth's environment. Land cover and land surface information are the most direct environmental information in remote sensing images, and they are also the basic data of other environmental elements. It is noted that remote sensing data has the characteristics of large description range, dynamic acquisition, and fast update speed. Therefore, it can be widely used in various fields of social development and national economy, which shows the...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/40G06F18/2411G06F18/214
Inventor 赵文智陈家阁柏延臣
Owner BEIJING NORMAL UNIVERSITY
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