结合卷积神经网络与显著性特征的机场检测

doi:10.13474/j.cnki.11-2246.2019.0216

测绘通报 ›› 2019, Vol. 0 ›› Issue (7): 44-49.doi: 10.13474/j.cnki.11-2246.2019.0216

结合卷积神经网络与显著性特征的机场检测

余东行¹, 张宁², 张保明¹, 郭海涛¹, 卢俊¹

1. 信息工程大学, 河南郑州 450001;
2. 中国卫星导航定位应用管理中心, 北京 100088

收稿日期:2018-10-12 出版日期:2019-07-25 发布日期:2019-07-31
作者简介:余东行(1993-),男,硕士生,主要研究方向为深度学习、遥感影像目标识别。E-mail:dong_hang@aliyun.com
基金资助:
国家自然科学基金（41601507）

Airport detection using convolutional neural network and salient feature

YU Donghang¹, ZHANG Ning², ZHANG Baoming¹, GUO Haitao¹, LU Jun¹

1. Information Engineering University, Zhengzhou 450001, China;
2. China National Administration of GNSS and Application, Beijing 100088, China

Received:2018-10-12 Online:2019-07-25 Published:2019-07-31

摘要/Abstract

摘要： 遥感影像机场检测中，针对传统人工设计特征的方法稳健性差、检测耗时的问题，提出了一种结合卷积神经网络与显著性特征的遥感影像机场检测算法。利用卷积神经网络快速准确地检测出机场目标，确定兴趣区域，对兴趣区域进行显著性检测和连通区提取，从而获取更加精确的机场边界，最后利用多种场景下的影像进行测试。结果表明，本文方法具有明显的精度和速度优势；利用频率视觉显著性分析方法对获得的机场区域进行视觉显著性检测，可有效获取机场和跑道的精确边界，提高机场检测的效果和实用价值。

关键词: 机场检测, 遥感影像, 卷积神经网络, 显著性特征

Abstract: Existing algorithms of airport detection using handcraft features perform time-consuming and poor robustness. In view of these problems, this paper proposes a method using convolutional neural network and salient feature. First, a deep convolutional neural network is used to extract the regions of interest (ROI) from complex remote sensing images. Then, saliency detection based on frequency-tuned is introduced to get saliency map of those regions. Through segment on the saliency map and marking the connected region on the binary image, the maximum connected region which is most likely be area of the airport is extracted. Different kinds of airports are used to test and the results show that the proposed method has obvious advantages in precision and speed. With the aid of saliency detection, the precise boundary of the airport and runway can be obtained effectively and the effect and practical value of the airport detection are hugely improved.

Key words: airport detection, remote sensing image, convolutional neural network, salient feature

中图分类号:

P237

余东行, 张宁, 张保明, 郭海涛, 卢俊. 结合卷积神经网络与显著性特征的机场检测[J]. 测绘通报, 2019, 0(7): 44-49.

YU Donghang, ZHANG Ning, ZHANG Baoming, GUO Haitao, LU Jun. Airport detection using convolutional neural network and salient feature[J]. 测绘通报, 2019, 0(7): 44-49.

参考文献

[1] TANG G F, XIAO Z F, LIU Q, et al. A novel airport detec-tion method via line segment classification and texture classification[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 12(12):2408-2412.
[2] BUDAK Ü, HALICI U, SENGÜR A, et al. Efficient airport detection using line segment detector and fisher vector representation[J].IEEE Geoscience and Remote Sensing Letters, 2016, 13(8):1079-1083.
[3] 韩晓青,杜红悦.边缘追踪模型与SURF检测结合提取天绘影像机场目标[J].中国图象图形学报,2016,21(10):1337-1347.
[4] 潘治鸿,窦浩,刘迪,等.基于直线和区域显著性融合机制的机场检测[J].计算机工程与应用,2018,54(8):154-159
[5] 王鑫,王斌,张立明.基于图像显著性区域的遥感图像机场检测[J].计算机辅助设计与图形学学报,2012,24(3):336-344.
[6] ZHANG L B, ZHANG Y Y. Airport detection and aircraft recognition based on two-layer saliency model in high spatial resolution remote sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,10(4):1511-1524.
[7] WANG Y, LI Pan. Automatic airport recognition based on saliency detection and semantic information[J]. ISPRS International Journal of Geo-Information, 2016,5(7):115.
[8] XIAO Z F, GONG Y P, LONG Y, et al. Airport detection based on a multiscale fusion feature for optical remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters,2017,14(9):1469-1473.
[9] 闫钧华,许俊峰,艾淑芳,等.基于局部多特征的机场跑道检测算法[J].仪器仪表学报,2014,35(8):1714-1720.
[10] CAI B, JIANG Z G, ZHANG H P, et al. Airport detection using end-to-end convolutional neural network with hard example mining[J]. Remote Sensing,2017,9(11):1198.
[11] ZHANG P, NIU X, DOU Y, et al. Airport detection on optical satellite images using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2017,14(8):1183-1187.
[12] ZHAO D P, MA Y Y, JIANG Z G, et al. Multiresolution airport detection via hierarchical reinforcement learning saliency model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,10(6):2855-2866.
[13] 朱明明,许悦雷,马时平,等.改进基于区域的卷积神经网络的机场检测方法[J].光学学报,2018,38(7):0728001.
[14] 辛鹏,许悦雷,马时平,等.区域提取网络结合自适应池化网络的机场检测[J].西安电子科技大学学报,2018,45(3):102-108.
[15] REDMON J,DIVVALA S,GIRSHICK R, et al.You only look once:unified, real-time object detection[EB/OL].[2018-09-11]. https://arxiv.org/abs/1506.02640v5
[16] REDMON J, FARHADI A. YOLO9000:better, faster, stronger[EB/OL].[2018-10-09].https://arxiv.org/abs/1612.08242.
[17] REDMON J,FARHADI A. YOLOv3:an incremental improvement[EB/OL].[2018-10-09].https://arxiv.org/abs/1804.02767.
[18] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN:towards realtime object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,39(6):1137-1149.
[19] ACHANTAY R, HEMAMIZ S, ESTRADAY F, et al. Frequency-tuned salient region detection[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miani:IEEE, 2009.
[20] LIN T Y, GOYAL P, et al. Focal loss for dense object detection[EB/OL].[2018-10-09].https://arxiv.org/abs/1708.02002.

结合卷积神经网络与显著性特征的机场检测

Airport detection using convolutional neural network and salient feature

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	柳林, 孙毅, 李万武. 基于CNN海上钻井平台检测模型的构建及训练算法分析[J]. 测绘通报, 2022, 0(7): 26-32,99.
[2]	蔡柔丹. 一种基于用户异步轨迹的身份识别智能方法[J]. 测绘通报, 2022, 0(7): 158-162,167.
[3]	滕文鑫, 张建辰, 邵杰. 集成时空邻近与卷积网络车道级高精度定位算法[J]. 测绘通报, 2022, 0(6): 1-5,61.
[4]	李茂林, 闫庆武, 仲晓雅. 一种复合型校正植被信息的方法[J]. 测绘通报, 2022, 0(6): 49-54142.
[5]	文奴, 郭仁忠, 贺彪, 万远. YOLO v4框架下Multi-Patch多帧增量式交通视频目标检测[J]. 测绘通报, 2022, 0(5): 38-44.
[6]	张龚泉, 岳建平, 刘胜男, 王海龙, 杨智翔, 周航宇. 基于遥感影像的鄱阳湖地形提取方法[J]. 测绘通报, 2022, 0(5): 62-66,73.
[7]	潘建平, 付占宝, 程卫华, 陆世安, 李鑫, 孙博文, 黄桂萍. 基于地表类型的山地海绵城市生态建设评估[J]. 测绘通报, 2022, 0(5): 84-88.
[8]	陈智朗, 付振华, 朱紫阳, 王慧慧, 刘沁雯, 杨钰灵, 许耿然. 基于HRNet的高分辨率遥感影像建筑物变化信息提取[J]. 测绘通报, 2022, 0(5): 126-132.
[9]	刘小玉, 刘扬, 杜明义, 张敏, 贾竞珏, 杨恒. 基于DeeplabV3+的建筑垃圾堆放点识别[J]. 测绘通报, 2022, 0(4): 16-19,43.
[10]	王雪, 梁珂, 隋立春, 钟棉卿, 朱剑锋. 膨胀卷积与金字塔表达的深度学习模型用于农村建筑物提取[J]. 测绘通报, 2022, 0(4): 61-65.
[11]	高鸣, 周鑫鑫, 刘琦, 杨光迪, 吴长彬. 深度学习网络支持下的农房侵占耕地自动化监测[J]. 测绘通报, 2022, 0(3): 47-53.
[12]	王胜利, 朱寿红, 蒋毅. 基于CNN模型迁移的OLI影像光伏电池板场景识别[J]. 测绘通报, 2022, 0(2): 5-9.
[13]	杨素妨, 杜林. 多特征流形鉴别嵌入的高分辨率遥感影像分类[J]. 测绘通报, 2021, 0(9): 37-42.
[14]	耿万轩, 周维勋, 金双根. 基于LDCNN特征提取的多核SVM高分辨率遥感影像场景分类[J]. 测绘通报, 2021, 0(8): 14-21.
[15]	赵恒谦, 杨屹峰, 刘泽龙, 宋瑞. 农作物叶片病害迁移学习分步识别方法[J]. 测绘通报, 2021, 0(7): 34-38.