面向小规模面元天体数据集的二阶段边缘检测方法

郑洋; 李丹; 梁祖仲; 程永; 郑中杰

doi:10.11728/cjss2025.04.2024-0086

面向小规模面元天体数据集的二阶段边缘检测方法

doi: 10.11728/cjss2025.04.2024-0086 cstr: 32142.14.cjss.2024-0086

郑洋^1,,
李丹¹,
梁祖仲¹,
程永³,
郑中杰^{1, 2, ,}

1.
广东海洋大学数学与计算机学院　湛江　524088
2.
暨南大学中法天体测量、动力学与空间科学联合研究实验室　广州　510632
3.
北京青苔数据科技有限公司　北京　100071

基金项目: 国家重点研发计划项目(2022YFE0116800), 广东自然科学基金项目(2023A151501127)和教育部科技发展中心产学研创新基金项目(2020QT13)共同资助

详细信息

作者简介:

郑洋　男, 1996年7月出生于广东省茂名市, 现为广东海洋大学数学与计算机学院助教, 主要研究方向为空间天体测量技术、计算机视觉. E-mail: wlwzy@gdou.edu.cn

通讯作者:

郑中杰　男, 1988年1月出生于广东省茂名市, 现为广东海洋大学数学与计算机学院讲师, 主要研究方向为图像处理、高精度天体测量. E-mail: azhengzj@gdou.edu.cn

中图分类号: P121
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出版历程
- 收稿日期: 2024-07-03
- 修回日期: 2025-04-15
- 网络出版日期: 2025-05-12

Two-stage Contour Detection Method for Small-scale Disk-resolved Celestial Datasets

1.
School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang 524088
2.
Sino-French Joint Laboratory for Astrometry, Dynamics and Space Science, Jinan University, Guangzhou 510632
3.
Beijing Qingtai Data Technology Co., Ltd., Beijing 100071

摘要

摘要: 提出了一种面向小规模面元天体数据集的二阶段边缘检测方法. 第一阶段采用基于数字图像处理技术的窗口–边缘检测法进行初步边缘提取. 第二阶段则引入基于ResNet调整后的LPC-ResNet分类器, 用于补充第一阶段中可能遗漏的边缘点. 对新视野号远程勘测成像仪图像中的冥王星和冥卫一的边缘检测实验结果表明, 窗口–边缘检测法在精确率方面表现最佳, 即其去除非边缘点的能力最强, 但召回率较低. 通过引入LPC-ResNet, 召回率与F₁分数均有提升, 从而增强了对边缘点的保留能力, 使提取的边缘更完整. 此外, 基于提取的边缘对冥王星和冥卫一进行的中心测量应用实验显示, 二阶段方法相较于单一的窗口–边缘检测法具有更高的检测精度, 证实了LPC-ResNet可有效改善窗口–边缘检测法的边缘提取结果.
- 边缘检测 /
- ResNet /
- 新视野号远程勘测成像仪图像 /
- 面元天体
Abstract: In the astrometry of the disk resolved objects, contour detection acts as an important step, and it is equivalent to a binary classification process of classifying pixel points as contour points and non-contour points. For the purpose of automated extraction of hierarchical contour features, enhancement of contour detection precision, and accommodation of either limited-scale disk-resolved celestial datasets or preliminary-phase observational campaigns, a two-stage contour detection method for small-scale disk-resolved celestial datasets is proposed in this paper. In the first stage, a window contour detection method utilizing digital image processing techniques and comprising three operational steps: window extraction, window searching, and elliptical fitting, is employed to perform initial contour extraction. The second stage introduces an LPC-ResNet classifier, an adapted version of ResNet architecture, to supplement potential contour points missed by the window-contour detection method in the first stage. Experimental validation using New Horizons’ Long-Range Reconnaissance Imager (LORRI) images of Pluto and its biggest satellite Charon demonstrates that the window-based method achieves optimal Precision, indicating its superior capability in filtering non-contour pixels, yet exhibits limited Recall, likely due to unintended exclusion of contour points during elliptical fitting in the window-contour detection method. After incorporating the LPC-ResNet classifier, both Recall and F₁ score are improved, enhancing the ability to retain contour points and resulting in more complete contour extraction. To further validate the role of LPC-ResNet classifier in refining detection outcomes, centroid measurement experiment of Pluto and Charon is conducted using the contours extracted by the two-stage detection method and the single window-contour detection method, using the ephemerides Plu060 as the reference. The results of the experiment demonstrate that the two-stage method reduces mean and standard deviation of the Observed-minus-Calculated (O-C) residuals in both x and y directions, which means that the two-stage method has higher detection precision compared to the single window-contour detection method, confirming that LPC-ResNet classifier can improve the contour extraction effect of the window-contour detection method.
- Contour detection /
- ResNet /
- New Horizons’ Long-Range Reconnaissance Imager images /
- Disk resolved objects
注释:

1) 1https://pds-imaging.jpl.nasa.gov/search/

HTML全文

图 1 数据集图像. (a) 原图像, (b) Canny算子滤波图, 红色标记像素点为边缘点,蓝色标记像素点为非边缘点, (c) 真实边缘图

Figure 1. Dataset frame examples. (a) Original frame. (b) Canny operator filtered frame. The red marked pixels are contour points, and the blue marked pixels are non-contour points. (c) True contour frame

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图 2 面向小规模天体面元数据集的二阶段边缘提取方法的流程. (a) 第一阶段, 通过执行窗口–边缘检测法获得初步的边缘. (b) 第二阶段, 使用LPC-ResNet分类器补充第一阶段遗漏的边缘点

Figure 2. Process flowchart for a two-stage contour detection method for small-scale disk-resolved celestial datasets. (a) The first stage, where preliminary contours are obtained by performing the window-contour detection method. (b) The second stage, where the LPC-ResNet classifier is used to supplement the contour points missed by the first stage

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图 3 窗口提取的例子. (a) 原图像, (b) 滤波图像, (c) X-边缘分布$ M\left(x\right) $结果, (d) Y-边缘分布$ M\left(y\right) $结果

Figure 3. Example of window extraction. (a) The original frame, (b) the filtered frame, (c) the X-marginal distribution result $ M\left(x\right) $, (d) the Y-marginal distribution result $ M\left(y\right) $

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图 4 窗口–边缘检测法的结果. (a) 滤波图像的窗口. (b) Y方向扫描(升序). (c) Y方向扫描(降序).(d) X方向扫描(降序). (e) X方向扫描(升序). (f) 椭圆拟合后的结果

Figure 4. Results of the window-contour detection method. (a) Window of the filtered frame. (b) Scanning in Y direction (ascending order). (c) Scanning in Y direction (descending order). (d) Scanning in X direction (descending order). (e)Scanning in X direction (ascending order). (f) Results after ellipse fitting

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图 5 像素邻域数据的训练样本采集结果, 以7×7大小邻域为例. 右上的5幅子图为像素邻域数据经过数据增强后的结果, 包括90°, 180°, 270°旋转以及水平与垂直翻转

Figure 5. Results of training sample collection for pixel neighborhood data, taking a 7×7 neighborhood size as an example. The five subplots in the upper right corner represent the results of data augmentation on pixel neighborhood data, including 90°, 180°, and 270° rotations, as well as horizontal and vertical flips

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图 6 残差块结构

Figure 6. Residual block structure

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图 7 模型训练与测试流程

Figure 7. Flow chart of model training and testing

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图 8 使用不同方法检测的冥王星边缘结果. (a) 原图像, (b) 窗口–边缘检测法的结果, (c) LPC-ResNet检测结果, (d) 窗口–边缘检测法+LPC-ResNet检测结果, (e) Sobel算子结果, (f) Zhang等提出的方法结果

Figure 8. Contours of Pluto detected using different methods. (a) Original image, (b) the window-contour detection method result, (c) the LPC-ResNet detection result, (d) the window-contour detection method + LPC-ResNet detection result, (e) the Sobel operator result, (f) the result of method proposed by Zhang et al.

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表 4 不同方法的精确率、召回率和F₁分数的结果对比

Table 4. Comparison of Precision, Recall, and F₁Score results using different methods

边缘检测方法	精确率	召回率	F₁分数
窗口–边缘检测法	0.954	0.756	0.843
LPC-ResNet-18	0.903	0.965	0.933
LPC-ResNet-34	0.903	0.959	0.929
LPC-ResNet-50	0.900	0.934	0.916
LPC-ResNet-101	0.885	0.903	0.892
LPC-ResNet-152	0.872	0.962	0.914
窗口–边缘检测+LPC-ResNet-18	0.916	0.963	0.938
窗口–边缘检测法+ LPC-ResNet-34	0.909	0.961	0.934
窗口–边缘检测法+ LPC-ResNet-50	0.887	0.967	0.925
窗口–边缘检测法+LPC-ResNet-101	0.895	0.956	0.925
窗口–边缘检测法+ LPC-ResNet-152	0.895	0.964	0.927
Zhang等提出的方法	0.771	0.746	0.758
Roberts 算子	0.827	0.572	0.676
Sobel 算子	0.829	0.644	0.725
Prewitt 算子	0.829	0.643	0.724

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表 1 LPC-ResNet的架构

Table 1. Architectures for LPC-ResNet

Layer_name	LPC-ResNet-18	LPC-ResNet-34	LPC-ResNet-50	LPC-ResNet-101	LPC-ResNet-152
conv1	3×3, 32	3×3, 16	3×3, 8	3×3, 8	3×3, 8
conv2_x	3×3 max pool
conv2_x	$ \left[ \begin{array}{c}3\times 3, 32\\ 3\times 3, 32\end{array} \right] $×2	$ \left[ \begin{array}{c}3\times 3, 16\\ 3\times 3, 16\end{array} \right] $×3	$ \left[ \begin{array}{c}1\times 1, 8\\ \begin{array}{c}3\times 3, 8\\ 3\times 3, 32\end{array}\end{array} \right] $×3	$ \left[ \begin{array}{c}1\times 1, 8\\ \begin{array}{c}3\times 3, 8\\ 3\times 3, 32\end{array}\end{array} \right] $×3	$ \left[ \begin{array}{c}1\times 1, 8\\ \begin{array}{c}3\times 3, 8\\ 3\times 3, 32\end{array}\end{array} \right] $×3
conv3_x	$ \left[ \begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array} \right] $×2	$ \left[ \begin{array}{c}3\times 3, 32\\ 3\times 3, 32\end{array} \right] $×4	$ \left[ \begin{array}{c}1\times 1, 16\\ \begin{array}{c}3\times 3, 16\\ 3\times 3, 64\end{array}\end{array} \right] $×4	$ \left[ \begin{array}{c}1\times 1, 16\\ \begin{array}{c}3\times 3, 16\\ 3\times 3, 64\end{array}\end{array} \right] $×4	$ \left[ \begin{array}{c}1\times 1, 16\\ \begin{array}{c}3\times 3, 16\\ 3\times 3, 64\end{array}\end{array} \right] $×8
conv4_x	$ \left[ \begin{array}{c}3\times 3, 128\\ 3\times 3, 128\end{array} \right] $×2	$ \left[ \begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array} \right] $×6	$ \left[ \begin{array}{c}1\times 1, 32\\ \begin{array}{c}3\times 3, 32\\ 3\times 3, 128\end{array}\end{array} \right] $×6	$ \left[ \begin{array}{c}1\times 1, 32\\ \begin{array}{c}3\times 3, 32\\ 3\times 3, 128\end{array}\end{array} \right] $×23	$ \left[ \begin{array}{c}1\times 1, 32\\ \begin{array}{c}3\times 3, 32\\ 3\times 3, 128\end{array}\end{array} \right] $×36
conv5_x	$ \left[ \begin{array}{c}3\times 3, 256\\ 3\times 3, 256\end{array} \right] $×2	$ \left[ \begin{array}{c}3\times 3, 128\\ 3\times 3, 128\end{array} \right] $×3	$ \left[ \begin{array}{c}1\times 1, 64\\ \begin{array}{c}3\times 3, 64\\ 3\times 3, 256\end{array}\end{array} \right] $×3	$ \left[ \begin{array}{c}1\times 1, 64\\ \begin{array}{c}3\times 3, 64\\ 3\times 3, 256\end{array}\end{array} \right] $×3	$ \left[ \begin{array}{c}1\times 1, 64\\ \begin{array}{c}3\times 3, 64\\ 3\times 3, 256\end{array}\end{array} \right] $×3
Output	average pool, 2 d-fc, softmax

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表 2 不同结构与像素邻域尺寸条件下的LPC-ResNet测试结果 (准确率/召回率/F₁分数)

Table 2. Test results of LPC-ResNet under different numbers of layers and different pixel neighborhood sizes (Precision/Recall/F₁Score)

结构	$ \mathrm{像}\mathrm{素}\mathrm{邻}\mathrm{域}\mathrm{尺}\mathrm{寸} $
结构	$ 3\times 3 $	$ 5\times 5 $	$ 7\times 7 $	$ 9\times 9 $	平均值
LPC-ResNet-18	0.926 / 0.966 / 0.946	0.931 / 0.944 / 0.937	0.852 / 0.983 / 0.913	0.903 / 0.968 / 0.934	0.903 / 0.965 / 0.933
LPC-ResNet-34	0.936 / 0.920 / 0.928	0.901 / 0.980 / 0.939	0.919 / 0.957 / 0.937	0.854 / 0.979 / 0.912	0.903 / 0.959 / 0.929
LPC-ResNet-50	0.918 / 0.922 / 0.920	0.859 / 0.949 / 0.901	0.926 / 0.892 / 0.909	0.896 / 0.973 / 0.933	0.900 / 0.934 / 0.916
LPC-ResNet-101	0.876 / 0.947 / 0.910	0.881 / 0.882 / 0.881	0.923 / 0.847 / 0.883	0.859 / 0.934 / 0.895	0.885 / 0.903 / 0.892
LPC-ResNet-152	0.925 / 0.918 / 0.922	0.885 / 0.978 / 0.929	0.814 / 0.986 / 0.892	0.865 / 0.965 / 0.912	0.872 / 0.962 / 0.914
平均值	0.916 / 0.935 / 0.925	0.891 / 0.947 / 0.917	0.887 / 0.933 / 0.907	0.875 / 0.964 / 0.917	―

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表 3 不同结构与像素邻域尺寸条件下的二阶段检测方法的测试结果 (准确率/召回率/F₁分数)

Table 3. Test results of two-stage detection method under different numbers of layers and different pixel neighborhood sizes (Precision/Recall/ F₁Score)

结构	$ \mathrm{像}\mathrm{素}\mathrm{邻}\mathrm{域}\mathrm{尺}\mathrm{寸} $
结构	$ 3\times 3 $	$ 5\times 5 $	$ 7\times 7 $	$ 9\times 9 $	平均值
窗口–边缘检测+LPC-ResNet-18	0.941 / 0.945 / 0.943	0.910 / 0.982 / 0.944	0.893 / 0.970 / 0.930	0.920 / 0.954 / 0.936	0.916 / 0.963 / 0.938
窗口–边缘检测+LPC-ResNet-34	0.936 / 0.939 / 0.937	0.873 / 0.982 / 0.925	0.906 / 0.950 / 0.927	0.919/ 0.973 / 0.945	0.909 / 0.961 / 0.934
窗口–边缘检测 +LPC-ResNet-50	0.912 / 0.962 / 0.937	0.881 / 0.960 / 0.919	0.899 / 0.978 / 0.937	0.855 / 0.969 / 0.908	0.887/ 0.967 / 0.925
窗口–边缘检测+LPC-ResNet-101	0.904 / 0.936 / 0.920	0.881/ 0.963 / 0.920	0.905 / 0.960 / 0.932	0.890 / 0.966 / 0.926	0.895 / 0.956 / 0.925
窗口–边缘检测+LPC-ResNet-152	0.901/ 0.968 / 0.933	0.855 / 0.993 / 0.919	0.899 / 0.955 / 0.926	0.923 / 0.939 / 0.931	0.895 / 0.964 / 0.927
平均值	0.919/ 0.950 / 0.934	0.880 / 0.976 / 0.925	0.900 / 0.963 / 0.930	0.901 / 0.960 / 0.929	―

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表 5 窗口–边缘检测法与二阶段检测方法在$ \mathit{x} $和$ \mathit{y} $方向的 (O-C) 误差的均值与标准差 (以Plu060历表为参考)

Table 5. Mean and standard deviation of (O-C) errors in $ \mathit{x} $ and $ \mathit{y} $ directions of the window-contour detection method and the two-stage detection method, using the Plu060 ephemeris as reference

测量指标	窗口–边缘检测法	窗口–边缘检测法+LPC-ResNet
$ x $方向(O-C)误差均值$ /\mathrm{k}\mathrm{m} $	–25.13	–11.52
$ x $方向(O-C)误差标准差$ /\mathrm{k}\mathrm{m} $	74.86	66.56
$ y $方向(O-C)误差均值$ /(\mathrm{k}\mathrm{m} $	–16.55	–10.81
$ y $方向(O-C)误差标准差$ /\mathrm{k}\mathrm{m} $	39.27	38.00

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面向小规模面元天体数据集的二阶段边缘检测方法

doi: 10.11728/cjss2025.04.2024-0086 cstr: 32142.14.cjss.2024-0086

作者简介:

郑洋　男, 1996年7月出生于广东省茂名市, 现为广东海洋大学数学与计算机学院助教, 主要研究方向为空间天体测量技术、计算机视觉. E-mail: wlwzy@gdou.edu.cn

通讯作者:

郑中杰　男, 1988年1月出生于广东省茂名市, 现为广东海洋大学数学与计算机学院讲师, 主要研究方向为图像处理、高精度天体测量. E-mail: azhengzj@gdou.edu.cn

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Two-stage Contour Detection Method for Small-scale Disk-resolved Celestial Datasets

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留言板

面向小规模面元天体数据集的二阶段边缘检测方法

doi: 10.11728/cjss2025.04.2024-0086 cstr: 32142.14.cjss.2024-0086

作者简介: 郑洋 男, 1996年7月出生于广东省茂名市, 现为广东海洋大学数学与计算机学院助教, 主要研究方向为空间天体测量技术、计算机视觉. E-mail: wlwzy@gdou.edu.cn

通讯作者: 郑中杰 男, 1988年1月出生于广东省茂名市, 现为广东海洋大学数学与计算机学院讲师, 主要研究方向为图像处理、高精度天体测量. E-mail: azhengzj@gdou.edu.cn

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Two-stage Contour Detection Method for Small-scale Disk-resolved Celestial Datasets

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出版历程

目录

作者简介:

郑洋　男, 1996年7月出生于广东省茂名市, 现为广东海洋大学数学与计算机学院助教, 主要研究方向为空间天体测量技术、计算机视觉. E-mail: wlwzy@gdou.edu.cn

通讯作者:

郑中杰　男, 1988年1月出生于广东省茂名市, 现为广东海洋大学数学与计算机学院讲师, 主要研究方向为图像处理、高精度天体测量. E-mail: azhengzj@gdou.edu.cn