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基于深度學(xué)習的衛星遙感圖像邊緣檢測方法
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吉林大學(xué) 地球探測科學(xué)與技術(shù)學(xué)院

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National natural science foundation of China (42171407 ,42077242)


An edge detection method of satellite remote sensing image based on deep learning
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    摘要:

    為解決衛星遙感圖像邊緣模糊噪點(diǎn)過(guò)多,導致圖像清晰度過(guò)低的問(wèn)題,提出基于深度學(xué)習的衛星遙感圖像邊緣檢測方法。利用Softmax分類(lèi)器結構,提取邊緣圖像節點(diǎn)處的數據信息參量,遵循深度學(xué)習算法,完成對圖像信息的卷積與池化處理,實(shí)現基于深度學(xué)習的衛星遙感圖像識別。根據尺度空間定義原則,確定邊緣檢測特征點(diǎn)所處位置,再聯(lián)合梯度信息熵計算結果,完成對衛星遙感圖像的拼接處理。分別計算一階微分邊緣算子、二階微分邊緣算子的具體數值,確定梯度幅值的取值區間,總結已知數值參量,建立完整的雙閾值表達式,完成基于深度學(xué)習的衛星遙感圖像邊緣檢測方法的設計。實(shí)驗結果表明,應用所提方法后衛星遙感圖像邊緣節點(diǎn)處信噪比指標增大,可有效控制模糊噪點(diǎn)對圖像清晰度的影響,在衛星遙感圖像邊緣精準檢測方面具有較強的實(shí)用性。

    Abstract:

    In order to solve the problem of too much blurred noise at the edge of satellite remote sensing image, which leads to low image definition, an edge detection method of satellite remote sensing image based on deep learning is proposed. Using the Softmax classifier structure, the data information parameters at the edge image nodes are extracted, and the deep learning algorithm is followed to complete the convolution and pooling of image information, and realize the recognition of satellite remote sensing images based on deep learning. According to the definition principle of scale space, the location of edge detection feature points is determined, and the result of gradient information entropy calculation is combined to complete the splicing of satellite remote sensing images. Calculate the specific values ??of the first-order differential edge operator and the second-order differential edge operator respectively, determine the value range of the gradient amplitude, summarize the known numerical parameters, establish a complete double-threshold expression, and complete the satellite remote sensing image based on deep learning. Design of edge detection methods. The experimental results show that the signal-to-noise ratio index at the edge nodes of satellite remote sensing images increases after the application of the proposed method, which can effectively control the impact of blurred noise on image clarity, and has strong practicability in accurate edge detection of satellite remote sensing images.

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葉應輝.基于深度學(xué)習的衛星遙感圖像邊緣檢測方法計算機測量與控制[J].,2022,30(10):39-44.

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歷史
  • 收稿日期:2022-05-05
  • 最后修改日期:2022-06-22
  • 錄用日期:2022-06-23
  • 在線(xiàn)發(fā)布日期: 2022-11-01
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