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基于改進(jìn)區域分割遙感圖像的航天器目標自動(dòng)識別方法
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成都理工大學(xué) 工程技術(shù)學(xué)院

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Automatic recognition method of spacecraft target based on improved region segmentation remote sensing image
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    摘要:

    :傳統的航天器目標自動(dòng)識別方法識別精準度差,為了解決這一問(wèn)題,基于改進(jìn)區域分割遙感圖像研究了一種新的航天器目標自動(dòng)識別方法,通過(guò)人工排查的方式來(lái)追蹤航天器所提供的位置信息,并建立三角形立體體系,提取出航天器所追蹤的目標和航天器之間的位置關(guān)系,實(shí)現航天器目標檢測,分別針對復雜場(chǎng)景和運動(dòng)場(chǎng)景對目標進(jìn)行識別,引用擊穿識別方法,基于遺傳算法以及變換算法,實(shí)現了在復雜的自然遙感圖像中能夠識別多種目標,但是對于殘缺和不完整的目標識別性差,因此又在方法中引入了自動(dòng)學(xué)習智能識別算法,解決了在遙感圖像中殘缺不完整的目標識別效果差的問(wèn)題。設定對比實(shí)驗,結果表明,相較于傳統方法,基于改進(jìn)區域分割遙感圖像的航天器目標自動(dòng)識別方法識別準確率提高了15.23%。

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    The traditional automatic recognition method of spacecraft target has poor recognition accuracy. In order to solve this problem, a new automatic recognition method of spacecraft target is studied based on the improved regional segmentation remote sensing image. The position information provided by the spacecraft is tracked by manual screening, and the triangle stereo system is established to extract the target tracked by the spacecraft and the target of the spacecraft In order to realize the detection of spacecraft targets, the position relationship between them is used to recognize the targets in complex scenes and moving scenes respectively. The method of breakdown recognition is used. Based on genetic algorithm and transformation algorithm, the recognition of multiple targets in complex natural remote sensing images is realized. However, the recognition of incomplete and incomplete targets is poor, so the automatic learning intelligence is introduced into the method The recognition algorithm can solve the problem that the recognition effect of incomplete target in remote sensing image is poor. The experimental results show that the recognition accuracy of the spacecraft target automatic recognition method based on the improved region segmentation remote sensing image is improved by 15.23% compared with the traditional method.

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嚴南,姚捃,黃宇.基于改進(jìn)區域分割遙感圖像的航天器目標自動(dòng)識別方法計算機測量與控制[J].,2020,28(10):151-154.

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歷史
  • 收稿日期:2020-02-24
  • 最后修改日期:2020-04-07
  • 錄用日期:2020-04-07
  • 在線(xiàn)發(fā)布日期: 2020-10-21
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