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High resolution remote sensing image local feature extraction method based on 2D-Gabor

A 2d-gabo, remote sensing image technology, applied in the field of high-resolution remote sensing image processing, can solve the problem of lack of remote sensing image selection process, etc.

Active Publication Date: 2015-09-02
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

This method lacks the process of selecting the frequency direction of remote sensing images, and the features with obvious direction changes in the frequency domain will be ignored.

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  • High resolution remote sensing image local feature extraction method based on 2D-Gabor
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  • High resolution remote sensing image local feature extraction method based on 2D-Gabor

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Embodiment Construction

[0054] Below in conjunction with accompanying drawing and embodiment the present invention will be further described:

[0055] Such as figure 1 shown.

[0056] S1. Use 2D-Gabo to generate the scale space of remote sensing images. The kernel function of the 2D-Gabor is that the direction angle is θ and the frequency is w 0 The complex sine function of modulates the two-dimensional Gaussian function h(x,y,θ,σ x ,σ y ), the scale space is L(x,y,σ s ), where h(x,y,θ,σ x ,σ y )=g(x',y')exp[2πj(u 0 x+ν 0 y)],

[0057] L ( x , y , σ s ) = Π m = 1 N | L ( x , y , σ s , θ ...

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Abstract

The invention belongs to the field of high resolution remote sensing image processing and particularly relates to a high resolution remote sensing image local feature extraction method based on 2D-Gabor. According to the method provided by the invention, a scale space pyramid expression of an image is firstly established; accelerated partition testing features of different feature scales are searched in the scale space, and a maximum value inhibition method is utilized to obtain a feature point and to determine the position and the scale of the feature point; then a local feature descriptor based on a binary system is established; and finally, a Hamming distance is used in a similarity measurement method to perform feature matching of images of the same scene under different perspective conditions, then an RANSAC algorithm is adopted to perform feature purification, and error matching point pairs are removed. The method provided by the invention can accurately simulate cognitive features of the visual cortex and the retina of human beings. In the feature detection process, an invariance property for change in brightness and scale is achieved, and optimal performances can be obtained at the same time in the time domain and the frequency domain.

Description

technical field [0001] The invention belongs to the field of high-resolution remote sensing image processing, in particular to a method for extracting local features of high-resolution remote sensing images based on 2D-Gabor. Background technique [0002] With the development of remote sensing technology and the emergence of high-resolution remote sensing images, the details of remote sensing images are more abundant. Compared with ordinary images, the size of remote sensing images is larger, and the distribution of data information is more complex. This complexity determines that a single feature extraction model cannot be used when processing remote sensing images. In addition, factors such as non-uniform illumination and oversaturation of remote sensing imaging will affect the traditional method based on global feature extraction. Using local feature detection and feature description methods, the local features obtained can only be highly abstracted from the entire image...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46
CPCG06V10/44
Inventor 许文波杨淼范肖肖张亚璇樊香所
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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