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基于自編碼神經(jīng)網(wǎng)絡(luò )的航空物探遙感數據分類(lèi)方法研究
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上海航空工業(yè)(集團)有限公司

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Research on classification method of airborne geophysical remote sensing data based on self-coding neural network
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    摘要:

    航空物探遙感數據的采集過(guò)程中受到電磁波輻射等外界因素的影響,導致航空物探遙感數據分類(lèi)準確率較低,為此提出基于自編碼神經(jīng)網(wǎng)絡(luò )的航空物探遙感數據分類(lèi)方。根據航空物探對象的基本特征,設置遙感數據的分類(lèi)標準。通過(guò)輻射校正、幾何糾正、噪聲消除等步驟,完成航空物探遙感數據的預處理。構建自編碼神經(jīng)網(wǎng)絡(luò ),利用自編碼神經(jīng)網(wǎng)絡(luò )算法,從光譜、形狀、紋理等方面提取遙感數據特征,通過(guò)特征匹配確定航空物探遙感數據的所屬類(lèi)型。通過(guò)分類(lèi)性能測試實(shí)驗得出結論:所提方法的全局遙感數據分類(lèi)成功率和錯誤率的平均值分別為99.8%和0.6%,局部遙感數據分類(lèi)的成功率和錯誤率的平均值分別為99.8%和0.3%,即所提方法在分類(lèi)性能方面具有明顯優(yōu)勢。

    Abstract:

    The acquisition process of airborne geophysical remote sensing data is affected by external factors such as electromagnetic wave radiation, resulting in low classification accuracy of airborne geophysical remote sensing data. Therefore, a classification method for airborne geophysical remote sensing data based on self coding neural network is proposed. Set classification standards for remote sensing data based on the basic characteristics of aerial geophysical exploration objects. Complete the preprocessing of airborne geophysical remote sensing data through radiation correction, geometric correction, noise elimination, and other steps. Construct a self coded neural network, use self coded neural network algorithms to extract features of remote sensing data from aspects such as spectrum, shape, texture, and determine the type of airborne geophysical remote sensing data through feature matching. Through classification performance testing experiments, it is concluded that the proposed method has an average classification success rate and error rate of 99.8% and 0.6% for global remote sensing data, and an average classification success rate and error rate of 99.8% and 0.3% for local remote sensing data, respectively, indicating that the proposed method has significant advantages in classification performance.

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于劉.基于自編碼神經(jīng)網(wǎng)絡(luò )的航空物探遙感數據分類(lèi)方法研究計算機測量與控制[J].,2024,32(3):253-258.

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歷史
  • 收稿日期:2023-04-19
  • 最后修改日期:2023-05-24
  • 錄用日期:2023-05-25
  • 在線(xiàn)發(fā)布日期: 2024-04-01
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