不同光照条件下含噪遥感图像边缘检测算法

doi:10.13229/j.cnki.jdxbgxb20211300

摘要/Abstract

摘要：

针对不同光照条件导致遥感图像采集过程中形成随机噪声的问题，研究不同光照条件下含噪遥感图像边缘检测算法，以此提升遥感图像配准、识别的精度。针对不同光照条件下遥感图像获取过程中的随机高斯噪声，将邻域均值滤波算法与中值滤波算法相结合构建加窗中值滤波算法，利用该算法对含噪遥感图像实施滤波处理，消除遥感图像内的各类噪声。针对去噪后的遥感图像，利用Canny算子进行图像梯度计算，并进行最大值约束。对约束后的遥感图像梯度直方图采用最大类间方差法自适应检测和连接边缘的高、低阈值，通过跟踪边缘像素点，完成遥感图像边缘检测。测试结果表明，该算法可最大限度上还原实际遥感图像，准确检测图像边缘信息，提升遥感图像配准、识别的精度。

关键词: 光照条件, 噪声, 遥感图像, 边缘检测, 滤波算法, 最大类间方差

Abstract:

Aiming at the problem of random Gaussian noise in the process of remote sensing image acquisition caused by different lighting conditions， this paper studies the edge detection algorithm of noisy remote sensing image under different lighting conditions， so as to improve the accuracy of remote sensing image registration and recognition. Aiming at the random Gaussian noise in the process of remote sensing image acquisition under different illumination conditions， a windowed median filtering algorithm is constructed by combining the neighborhood mean filtering algorithm with the median filtering algorithm. The algorithm is used to filter the noisy remote sensing image to eliminate all kinds of noise conditions in the remote sensing image. For the denoised remote sensing image， the Canny operator is used to calculate the image gradient and constrain the maximum value. For the constrained gradient histogram of remote sensing image， the maximum interclass variance method is used to adaptively detect and connect the high and low thresholds of edges， and the edge detection of remote sensing image is completed by tracking edge pixels. The test results show that the algorithm can return the actual remote sensing image to the greatest extent， accurately detect the image edge information， and improve the accuracy of remote sensing image registration and recognition.

Key words: illumination conditions, noisy, remote sensing images, edge detection, filtering algorithm, maximum interclass variance

中图分类号:

TP301

马为駽,张䶮,马传香,朱飒. 不同光照条件下含噪遥感图像边缘检测算法[J]. 吉林大学学报(工学版), 2023, 53(1): 241-247.

Wei-xuan MA,Yan ZHANG,Chuan-xiang MA,Sa ZHU. Edge detection algorithm of noisy remote sensing image under different illumination conditions[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 241-247.

图/表 6

图1

图2

图3

图4

图5

表1

参考文献 15

1	兰传琳, 方佩章, 何楚. 基于先验模型优化的无人机遥感图像中几何轮廓目标检测方法[J]. 电视技术, 2019, 43(1): 5-10, 65.
	Lan Chuan-lin, Fang Pei-zhang, He Chu. Geometric contour detection method in UAV remote sensing image based on prior model optimization[J]. Video Engineering, 2019, 43(1): 5-10, 65.
2	赵建鹏, 杨秀峰, 李国洪, 等. 基于面向对象的设施蔬菜高分遥感影像提取[J]. 江苏农业学报, 2019, 35(4): 911-918.
	Zhao Jian-peng, Yang Xiu-feng, Li Guo-hong, et al. Object oriented extraction of high resolution remote sensing images of facility vegetables[J]. Journal of Jiangsu Agriculture, 2019, 35(4): 911-918.
3	刘丽霞, 李宝文, 王阳萍, 等. 改进Canny边缘检测的遥感影像分割[J]. 计算机工程与应用, 2019, 55(12): 54-58, 180.
	Liu Li-xia, Li Bao-wen, Wang Yang-ping, et al. Remote sensing image segmentation based on improved Canny edge detection[J]. Computer Engineering and Applications, 2019, 55(12): 54-58, 180.
4	杨斌, 王翔. 基于深度残差去噪网络的遥感融合图像质量提升[J]. 激光与光电子学进展, 2019,56(16):88-97.
	Yang Bin, Wang Xiang. Boosting quality of pansharpened images using deep residual denoising network[J]. Laser & Optoelectronics Progress, 2019, 56(16): 88-97.
5	张文坤, 汪西原, 宋佳乾. 基于分数阶微分差与高斯曲率滤波的边缘检测算法[J]. 计算机工程, 2019, 45(2): 213-219.
	Zhang Wen-kun, Wang Xi-yuan, Song Jia-qian. Edge detection algorithm based on fractional differential difference and Gaussian curvature filtering[J]. Computer Engineering, 2019, 45(2): 213-219.
6	秦振涛, 杨茹. 基于结构性字典学习的毛儿盖遥感图像去噪研究[J]. 遥感技术与应用, 2019,34(4):793-798.
	Qin Zhen⁃tao, Yang Ru. Remote sensing image of Mao'ergai denoising based on structured dictionary learning[J]. Remote Sensing Technology and Application, 2019, 34(4): 793-798.
7	袁宇丽. 基于机器学习和方向模板的遥感图像边缘检测方法[J]. 内江师范学院学报, 2020,35(8):51-55.
	Yuan Yu-li. Remote sensing image edge detection method based on machine learning and haar template [J]. Journal of Neijiang Normal University, 2020, 35 (8): 51-55.
8	陈顺, 孟青青, 李登峰. 结合图像增强和改进Canny算子的遥感图像边缘检测[J]. 河南大学学报: 自然科学版, 2020, 50(5): 623-630.
	Chen Shun, Meng Qing-qing, Li Deng-feng. Remote sensing image edge detection combined with image enhancement and improved Canny[J]. Journal of Henan University (Natural Science), 2020,50(5): 623-630.
9	王小兵. 融合提升小波阈值与多方向边缘检测的矿区遥感图像去噪[J]. 国土资源遥感, 2020,32(4):46-52.
	Wang Xiao-bing. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Natural Resources, 2020, 32(4): 46-52.
10	王冬云, 唐楚, 鄂世举, 等. 基于导向滤波Retinex和自适应Canny的图像边缘检测[J]. 光学精密工程, 2021, 29(2): 443-451.
	Wang Dong-yun, Tang Chu, Shi-ju E, et al. Image edge detection based on guided filter Retinex and adaptive Canny[J]. Optics and Precision Engineering, 2021, 29(2): 443-451.
11	黄巍, 黄辉先, 徐建闽, 等. 基于Canny边缘检测思想的改进遥感影像道路提取方法[J]. 国土资源遥感, 2019, 31(1): 65-70.
	Huang Wei, Huang Hui-xian, Xu Jian-min, et al. An improved road extraction method for remote sensing images based on Canny edge detection[J]. Remote Sensing for Natural Resources, 2019, 31(1): 65-70.
12	王小鹏, 文昊天, 王伟, 等. 形态学边缘检测和区域生长相结合的遥感图像水体分割[J]. 测绘科学技术学报, 2019, 36(2): 149-154, 160.
	Wang Xiao-peng, Wen Hao-tian, Wang Wei, et al. Water segmentation of remote sensing image using morphological edge detection and region growing[J]. Journal of Geomatics Science and Technology, 2019, 36(2): 149-154, 160.
13	张洪群, 顾吟雪, 郭擎. 灰色关联分析与模糊推理边缘检测图像融合法[J]. 遥感信息, 2020, 35(1): 15-27.
	Zhang Hong-qun, Gu Yin-xue, Guo Qing. Image fusion based edge detection of grey relational analysis and fuzzy inference[J] Remote Sensing Information, 2020, 35(1): 15-27.
14	苑希民, 韩超, 徐浩田, 等. 基于分形理论与SVM的河冰高分遥感影像智能识别方法研究[J]. 自然灾害学报, 2021, 30(2): 117-126.
	Yuan Xi-min, Han Chao, Xu Hao-tian, et al. Research on intelligent recognition method of river ice remote sensing image based on fractal theory and SVM [J]. Journal of Natural Disasters, 2021, 30(2): 117-126.
15	吴从中, 陈曦, 詹曙. 结合残差编解码网络和边缘增强的遥感图像去噪[J]. 遥感学报, 2020, 24(1): 27-36.
	Wu Cong-zhong, Chen Xi, Zhan Shu. Remote sensing image denoising using residual encoder-decoder networks with edge enhancement[J]. Journal of Remote Sensing, 2020, 24(1): 27-36.

相关文章 15

[1]	龙恩深,班光泽. 基于小波包信包提取的空调制冷压缩机怠速噪声诊断算法[J]. 吉林大学学报(工学版), 2023, 53(7): 1929-1934.
[2]	陈贵升,罗国焱,李靓雪,黄震,李一. 柴油机颗粒捕集器孔道流场及其高原环境下噪声特性分析[J]. 吉林大学学报(工学版), 2023, 53(7): 1892-1901.
[3]	陈晓雷,孙永峰,李策,林冬梅. 基于卷积神经网络和双向长短期记忆的稳定抗噪声滚动轴承故障诊断[J]. 吉林大学学报(工学版), 2022, 52(2): 296-309.
[4]	黄泰明,李伟平,胡涛涛,岳万昊,纪念洲,李域邦. 车用爪极发电机的气动噪声优化[J]. 吉林大学学报(工学版), 2022, 52(10): 2244-2255.
[5]	施昕昕,黄家才,高芳征. 基于分数阶BICO滤波器的运动控制测量噪声抑制[J]. 吉林大学学报(工学版), 2021, 51(5): 1873-1878.
[6]	李雄飞,吴佳婧,张小利,王泽宇,冯云丛. 基于相对总变差结构提取的遥感图像融合[J]. 吉林大学学报(工学版), 2021, 51(5): 1775-1784.
[7]	曾小华,宋美洁,宋大凤,王越. 基于车联网信息的公交客车行驶工况数据处理方法[J]. 吉林大学学报(工学版), 2021, 51(5): 1692-1699.
[8]	杨建,夏琦,周海超,王国林. 修正胎体弦轮廓载重子午线轮胎的降噪机理[J]. 吉林大学学报(工学版), 2021, 51(4): 1198-1203.
[9]	李健,刘孔宇,任宪盛,熊琦,窦雪峰. 基于自适应阈值的Canny算法在MRI边缘检测中的应用[J]. 吉林大学学报(工学版), 2021, 51(2): 712-719.
[10]	李静,石求军,洪良,刘鹏. 基于车辆状态估计的商用车ESC神经网络滑模控制[J]. 吉林大学学报(工学版), 2020, 50(5): 1545-1555.
[11]	陈鑫,王宁,沈传亮,冯晓,杨昌海. 后视镜造型对前侧窗气动噪声的影响[J]. 吉林大学学报(工学版), 2020, 50(2): 426-436.
[12]	赵鹏,蒋宇中,陈斌,李春腾,张杨勇. 基于局部方差域自适应Blanking的超低频信道噪声抑制方法[J]. 吉林大学学报(工学版), 2019, 49(5): 1696-1705.
[13]	王洪雁,房云飞,朱圣棋,裴炳南. 非均匀噪声条件下考虑互耦效应的DOA估计方法[J]. 吉林大学学报(工学版), 2019, 49(5): 1706-1714.
[14]	李雄飞,宋璐,张小利. 基于协同经验小波变换的遥感图像融合[J]. 吉林大学学报(工学版), 2019, 49(4): 1307-1319.
[15]	李居朋,张祖成,李墨羽,缪德芳. 基于Kalman滤波的电容屏触控轨迹平滑算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1910-1916.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

遥感图像	配准精度			识别精度
遥感图像	本文算法/%	基于深度残差网络的算法/%	基于高斯曲率滤波的算法/%	本文算法/%	基于深度残差网络的算法/%	基于高斯曲率滤波的算法/%
河流遥感图像	98.4	95.3	96.0	97.6	92.7	93.9
道路遥感图像	99.2	96.1	97.1	96.8	93.0	94.2
线路遥感图像	97.1	92.0	96.8	95.9	88.4	93.7
建筑遥感图像	98.9	95.7	96.7	96.1	91.5	95.2