Multi-sensor remote sensing image matching method

A remote sensing image and matching method technology, applied in the field of satellite image processing, can solve problems such as insufficient robustness, weak applicability of scale and rotation differences between images, and failure to use phase-consistent direction information, etc., to improve production efficiency and improve efficiency effect

Active Publication Date: 2016-01-20
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

Phase consistency is a feature extraction algorithm with illumination and contrast invariance. It has been widely used in the field of remote sensing image matching. However, the current method based on phase consistency is not suitable for scale and rotation differences between images. Moreover, only the eigenvalues ​​of phase consistency are used, but the direction information of phase consistency is not used, and the potential of phase consistency in feature extraction and description cannot be fully tapped, and the performance is not robust enough. Therefo

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[0024] The present invention will be further described below in conjunction with accompanying drawing:

[0025] A multi-sensor remote sensing image matching method, the specific steps of its realization are:

[0026] Step 1. Use Gaussian kernel functions of different scales σ to process the reference image I 1 and the image to be matched I 2 Filtering is performed to form a Gaussian scale space, and the DoG scale space is generated by the difference between two adjacent layers of images in the Gaussian scale space.

[0027] In the DoG space, compare each pixel of the middle layer (except the bottom and top layers) with 8 adjacent pixels of the same layer and 18 adjacent pixels of the upper and lower layers, a total of 26 pixels, if If the DoG value of the point is the largest or the smallest, it is considered as a candidate feature point, and then the point with low contrast and located on the edge is removed to obtain the final feature point PointIi (i=1,2,3,...,N ).

[0...

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Abstract

The invention discloses a multi-sensor remote sensing image matching method, belongs to the satellite image processing technology field and solves problems of geometric deformation and radiation difference of multi-sensor images. The method comprises steps that, a Gauss difference scale space for images is firstly established, and characteristic point detection is further carried out in the space; a phase consistency model having invariant illumination and contrast is introduced, phase consistency direction information is established and expanded for the phase consistency model, through a SIFT descriptor concept, a local characteristic descriptor-local phase consistency direction histogram is established by utilizing a phase consistency characteristic value and the characteristic direction; an European style distance between descriptors is taken as similarity measurement to carry out homonymy point identification, error matching points are eliminated, precise matching for the remote sensing images is realized. Compared with a traditional matching method, problems of geometric deformation and radiation difference of the remote sensing images can be effectively solved, image matching accuracy is improved, and the multi-sensor remote sensing image matching method is mainly applied to satellite image processing.

Description

technical field [0001] The invention belongs to the technical field of satellite image processing, in particular to the matching technology of remote sensing images. Background technique [0002] Image matching is essentially the process of identifying points with the same name between two or more images, and is widely used in remote sensing image registration, image stitching, and change detection. Due to the difference in imaging mechanism and spectral characteristics, there are often significant geometric and radiometric differences between multi-sensing remote sensing images, which makes it difficult to automatically identify points with the same name between images. [0003] Recently in the field of computer vision, local feature descriptors have been developed rapidly and widely used in image matching. The most famous local feature description is the ScaleInvariantFeatureTransform (SIFT) operator. Due to its rotation and scale invariance, SIFT has been widely used in ...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T2207/10032G06T2207/20212
Inventor 叶沅鑫慎利曹云刚
Owner SOUTHWEST JIAOTONG UNIV
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