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Detection method of small target of feature space

A technology of weak and small targets and detection methods, which is applied in image data processing, instruments, calculations, etc., can solve the problems of reducing the amount of calculation, large amount of calculation, complexity, etc., and achieve the effect of reducing the amount of calculation and making the detection simple and practical

Inactive Publication Date: 2009-09-30
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0003] In order to overcome the large amount of calculation and the complexity of the detection method in the prior art, the present invention provides a weak target detection method in the feature space, which fully considers the gray scale and motion characteristics of stars, targets and noise, and selects two stars as references. Dividing stars, potential targets and noise in the two-dimensional feature space domain can reduce the amount of calculation and realize weak target detection in the background of moving starry sky

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  • Detection method of small target of feature space
  • Detection method of small target of feature space

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Embodiment Construction

[0021] Concrete steps of the present invention are as follows:

[0022] 1. Star point positioning.

[0023] The main purpose of star point positioning is to represent stars, potential targets and noise spots (generally occupying one or several pixels) in the imaging image with precise position coordinates, which is convenient for later space mapping. From the previous analysis of grayscale characteristics, it can be seen that stars, targets and noise can be regarded as Gaussian surface distribution with symmetry, therefore, Gaussian fitting method can be used for accurate centroid extraction, but because Gaussian fitting method can improve the calculation accuracy At the same time, the calculation efficiency is indeed sacrificed. Therefore, the present invention finally selects the square weighted centroid extraction method for star point positioning. Firstly, top-hat filtering and Gaussian filtering are performed on the sequence starry sky image to remove the uneven illumina...

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Abstract

The invention discloses a detection method of a small target of a feature space, realizing small target detection of the feature space through steps of star position location, reference star selection, star and potential target division, track association cancelling noise, and the like. Because a detection algorithm of small moving target with a moving night sky background of a feature space based on distance is adopted, two stars are selected to be used as reference stars, different motion characteristics among the stars, and the target and a noise point are fully utilized to directly divide the stars, the potential target and the noise in a two-dimension feature space domain. Compared with three reference stars selected by the background technology on calculation of a similarity measuring function, an error square sum and a curve inflection point in a three-dimension space, the invention reduces the calculation amount because one star is decreased. Because the track continuity of the target is directly utilized to detect a track, a process that a potential target point set is mapped from the three-dimension space to the two-dimension space to be detected is avoided, and the detection is simpler and more practical.

Description

technical field [0001] The invention relates to a weak and small target detection method of a starry sky image, in particular to a weak and small target detection method in a feature space. Background technique [0002] The literature "Weak and Small Target Detection Algorithm Based on Hierarchical Clustering, Computer Engineering and Application, 2008.44(19): 24-27" proposed an algorithm for weak and small target detection using the idea of ​​hierarchical clustering. This method first selects 3 stars as reference stars, and based on the distance change between the star point and the reference star, a three-dimensional feature space is constructed through the distance difference. In this three-dimensional space, the similarity is constructed according to the motion characteristics of the star and the target. Metric function, find the optimal classification surface and the number of classifications by finding the sum of squared errors and the inflection point of the curve, an...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 张艳宁孙瑾秋姜磊段锋林增刚
Owner NORTHWESTERN POLYTECHNICAL UNIV
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